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udemy-ML/16-Boosted-Trees/01-Gradient-Boosting.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"___\n",
"\n",
"<a href='https://www.udemy.com/user/joseportilla/'><img src='../Pierian_Data_Logo.png'/></a>\n",
"___\n",
"<center><em>Copyright by Pierian Data Inc.</em></center>\n",
"<center><em>For more information, visit us at <a href='http://www.pieriandata.com'>www.pieriandata.com</a></em></center>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Gradient Boosting and GridSearch\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The Data\n",
"\n",
"\n",
"<img src=\"mushroom.jpg\" width=\"400\" height=\"400\">\n",
"\n",
"### Mushroom Hunting: Edible or Poisonous?\n",
"\n",
"Data Source: https://archive.ics.uci.edu/ml/datasets/Mushroom\n",
"\n",
"\n",
"This data set includes descriptions of hypothetical samples corresponding to 23 species of gilled mushrooms in the Agaricus and Lepiota Family (pp. 500-525). Each species is identified as definitely edible, definitely poisonous, or of unknown edibility and not recommended. This latter class was combined with the poisonous one. The Guide clearly states that there is no simple rule for determining the edibility of a mushroom; no rule like ``leaflets three, let it be'' for Poisonous Oak and Ivy.\n",
"\n",
"\n",
"Attribute Information:\n",
"\n",
"1. cap-shape: bell=b,conical=c,convex=x,flat=f, knobbed=k,sunken=s\n",
"2. cap-surface: fibrous=f,grooves=g,scaly=y,smooth=s\n",
"3. cap-color: brown=n,buff=b,cinnamon=c,gray=g,green=r, pink=p,purple=u,red=e,white=w,yellow=y\n",
"4. bruises?: bruises=t,no=f\n",
"5. odor: almond=a,anise=l,creosote=c,fishy=y,foul=f, musty=m,none=n,pungent=p,spicy=s\n",
"6. gill-attachment: attached=a,descending=d,free=f,notched=n\n",
"7. gill-spacing: close=c,crowded=w,distant=d\n",
"8. gill-size: broad=b,narrow=n\n",
"9. gill-color: black=k,brown=n,buff=b,chocolate=h,gray=g, green=r,orange=o,pink=p,purple=u,red=e, white=w,yellow=y\n",
"10. stalk-shape: enlarging=e,tapering=t\n",
"11. stalk-root: bulbous=b,club=c,cup=u,equal=e, rhizomorphs=z,rooted=r,missing=?\n",
"12. stalk-surface-above-ring: fibrous=f,scaly=y,silky=k,smooth=s\n",
"13. stalk-surface-below-ring: fibrous=f,scaly=y,silky=k,smooth=s\n",
"14. stalk-color-above-ring: brown=n,buff=b,cinnamon=c,gray=g,orange=o, pink=p,red=e,white=w,yellow=y\n",
"15. stalk-color-below-ring: brown=n,buff=b,cinnamon=c,gray=g,orange=o, pink=p,red=e,white=w,yellow=y\n",
"16. veil-type: partial=p,universal=u\n",
"17. veil-color: brown=n,orange=o,white=w,yellow=y\n",
"18. ring-number: none=n,one=o,two=t\n",
"19. ring-type: cobwebby=c,evanescent=e,flaring=f,large=l, none=n,pendant=p,sheathing=s,zone=z\n",
"20. spore-print-color: black=k,brown=n,buff=b,chocolate=h,green=r, orange=o,purple=u,white=w,yellow=y\n",
"21. population: abundant=a,clustered=c,numerous=n, scattered=s,several=v,solitary=y\n",
"22. habitat: grasses=g,leaves=l,meadows=m,paths=p, urban=u,waste=w,woods=d"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv(\"../DATA/mushrooms.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>class</th>\n",
" <th>cap-shape</th>\n",
" <th>cap-surface</th>\n",
" <th>cap-color</th>\n",
" <th>bruises</th>\n",
" <th>odor</th>\n",
" <th>gill-attachment</th>\n",
" <th>gill-spacing</th>\n",
" <th>gill-size</th>\n",
" <th>gill-color</th>\n",
" <th>...</th>\n",
" <th>stalk-surface-below-ring</th>\n",
" <th>stalk-color-above-ring</th>\n",
" <th>stalk-color-below-ring</th>\n",
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" <th>spore-print-color</th>\n",
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" <td>w</td>\n",
" <td>o</td>\n",
" <td>p</td>\n",
" <td>k</td>\n",
" <td>s</td>\n",
" <td>u</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>e</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 23 columns</p>\n",
"</div>"
],
"text/plain": [
" class cap-shape cap-surface cap-color bruises odor gill-attachment \\\n",
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"\n",
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"0 w w p w \n",
"1 w w p w \n",
"2 w w p w \n",
"3 w w p w \n",
"4 w w p w \n",
"\n",
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"0 o p k s u \n",
"1 o p n n g \n",
"2 o p n n m \n",
"3 o p k s u \n",
"4 o e n a g \n",
"\n",
"[5 rows x 23 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data Prep"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"X = df.drop('class',axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"y = df['class']"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"X = pd.get_dummies(X,drop_first=True)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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" <th>cap-surface_g</th>\n",
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" <th>cap-surface_y</th>\n",
" <th>cap-color_c</th>\n",
" <th>cap-color_e</th>\n",
" <th>...</th>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" cap-shape_c cap-shape_f cap-shape_k cap-shape_s cap-shape_x \\\n",
"0 0 0 0 0 1 \n",
"1 0 0 0 0 1 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 1 \n",
"4 0 0 0 0 1 \n",
"\n",
" cap-surface_g cap-surface_s cap-surface_y cap-color_c cap-color_e ... \\\n",
"0 0 1 0 0 0 ... \n",
"1 0 1 0 0 0 ... \n",
"2 0 1 0 0 0 ... \n",
"3 0 0 1 0 0 ... \n",
"4 0 1 0 0 0 ... \n",
"\n",
" population_n population_s population_v population_y habitat_g \\\n",
"0 0 1 0 0 0 \n",
"1 1 0 0 0 1 \n",
"2 1 0 0 0 0 \n",
"3 0 1 0 0 0 \n",
"4 0 0 0 0 1 \n",
"\n",
" habitat_l habitat_m habitat_p habitat_u habitat_w \n",
"0 0 0 0 1 0 \n",
"1 0 0 0 0 0 \n",
"2 0 1 0 0 0 \n",
"3 0 0 0 1 0 \n",
"4 0 0 0 0 0 \n",
"\n",
"[5 rows x 95 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 p\n",
"1 e\n",
"2 e\n",
"3 p\n",
"4 e\n",
"Name: class, dtype: object"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train Test Split "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=101)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Gradient Boosting and Grid Search with CV"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.ensemble import GradientBoostingClassifier"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on class GradientBoostingClassifier in module sklearn.ensemble._gb:\n",
"\n",
"class GradientBoostingClassifier(sklearn.base.ClassifierMixin, BaseGradientBoosting)\n",
" | GradientBoostingClassifier(*, loss='deviance', learning_rate=0.1, n_estimators=100, subsample=1.0, criterion='friedman_mse', min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_depth=3, min_impurity_decrease=0.0, min_impurity_split=None, init=None, random_state=None, max_features=None, verbose=0, max_leaf_nodes=None, warm_start=False, presort='deprecated', validation_fraction=0.1, n_iter_no_change=None, tol=0.0001, ccp_alpha=0.0)\n",
" | \n",
" | Gradient Boosting for classification.\n",
" | \n",
" | GB builds an additive model in a\n",
" | forward stage-wise fashion; it allows for the optimization of\n",
" | arbitrary differentiable loss functions. In each stage ``n_classes_``\n",
" | regression trees are fit on the negative gradient of the\n",
" | binomial or multinomial deviance loss function. Binary classification\n",
" | is a special case where only a single regression tree is induced.\n",
" | \n",
" | Read more in the :ref:`User Guide <gradient_boosting>`.\n",
" | \n",
" | Parameters\n",
" | ----------\n",
" | loss : {'deviance', 'exponential'}, default='deviance'\n",
" | loss function to be optimized. 'deviance' refers to\n",
" | deviance (= logistic regression) for classification\n",
" | with probabilistic outputs. For loss 'exponential' gradient\n",
" | boosting recovers the AdaBoost algorithm.\n",
" | \n",
" | learning_rate : float, default=0.1\n",
" | learning rate shrinks the contribution of each tree by `learning_rate`.\n",
" | There is a trade-off between learning_rate and n_estimators.\n",
" | \n",
" | n_estimators : int, default=100\n",
" | The number of boosting stages to perform. Gradient boosting\n",
" | is fairly robust to over-fitting so a large number usually\n",
" | results in better performance.\n",
" | \n",
" | subsample : float, default=1.0\n",
" | The fraction of samples to be used for fitting the individual base\n",
" | learners. If smaller than 1.0 this results in Stochastic Gradient\n",
" | Boosting. `subsample` interacts with the parameter `n_estimators`.\n",
" | Choosing `subsample < 1.0` leads to a reduction of variance\n",
" | and an increase in bias.\n",
" | \n",
" | criterion : {'friedman_mse', 'mse', 'mae'}, default='friedman_mse'\n",
" | The function to measure the quality of a split. Supported criteria\n",
" | are 'friedman_mse' for the mean squared error with improvement\n",
" | score by Friedman, 'mse' for mean squared error, and 'mae' for\n",
" | the mean absolute error. The default value of 'friedman_mse' is\n",
" | generally the best as it can provide a better approximation in\n",
" | some cases.\n",
" | \n",
" | .. versionadded:: 0.18\n",
" | \n",
" | min_samples_split : int or float, default=2\n",
" | The minimum number of samples required to split an internal node:\n",
" | \n",
" | - If int, then consider `min_samples_split` as the minimum number.\n",
" | - If float, then `min_samples_split` is a fraction and\n",
" | `ceil(min_samples_split * n_samples)` are the minimum\n",
" | number of samples for each split.\n",
" | \n",
" | .. versionchanged:: 0.18\n",
" | Added float values for fractions.\n",
" | \n",
" | min_samples_leaf : int or float, default=1\n",
" | The minimum number of samples required to be at a leaf node.\n",
" | A split point at any depth will only be considered if it leaves at\n",
" | least ``min_samples_leaf`` training samples in each of the left and\n",
" | right branches. This may have the effect of smoothing the model,\n",
" | especially in regression.\n",
" | \n",
" | - If int, then consider `min_samples_leaf` as the minimum number.\n",
" | - If float, then `min_samples_leaf` is a fraction and\n",
" | `ceil(min_samples_leaf * n_samples)` are the minimum\n",
" | number of samples for each node.\n",
" | \n",
" | .. versionchanged:: 0.18\n",
" | Added float values for fractions.\n",
" | \n",
" | min_weight_fraction_leaf : float, default=0.0\n",
" | The minimum weighted fraction of the sum total of weights (of all\n",
" | the input samples) required to be at a leaf node. Samples have\n",
" | equal weight when sample_weight is not provided.\n",
" | \n",
" | max_depth : int, default=3\n",
" | maximum depth of the individual regression estimators. The maximum\n",
" | depth limits the number of nodes in the tree. Tune this parameter\n",
" | for best performance; the best value depends on the interaction\n",
" | of the input variables.\n",
" | \n",
" | min_impurity_decrease : float, default=0.0\n",
" | A node will be split if this split induces a decrease of the impurity\n",
" | greater than or equal to this value.\n",
" | \n",
" | The weighted impurity decrease equation is the following::\n",
" | \n",
" | N_t / N * (impurity - N_t_R / N_t * right_impurity\n",
" | - N_t_L / N_t * left_impurity)\n",
" | \n",
" | where ``N`` is the total number of samples, ``N_t`` is the number of\n",
" | samples at the current node, ``N_t_L`` is the number of samples in the\n",
" | left child, and ``N_t_R`` is the number of samples in the right child.\n",
" | \n",
" | ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,\n",
" | if ``sample_weight`` is passed.\n",
" | \n",
" | .. versionadded:: 0.19\n",
" | \n",
" | min_impurity_split : float, default=None\n",
" | Threshold for early stopping in tree growth. A node will split\n",
" | if its impurity is above the threshold, otherwise it is a leaf.\n",
" | \n",
" | .. deprecated:: 0.19\n",
" | ``min_impurity_split`` has been deprecated in favor of\n",
" | ``min_impurity_decrease`` in 0.19. The default value of\n",
" | ``min_impurity_split`` has changed from 1e-7 to 0 in 0.23 and it\n",
" | will be removed in 0.25. Use ``min_impurity_decrease`` instead.\n",
" | \n",
" | init : estimator or 'zero', default=None\n",
" | An estimator object that is used to compute the initial predictions.\n",
" | ``init`` has to provide :meth:`fit` and :meth:`predict_proba`. If\n",
" | 'zero', the initial raw predictions are set to zero. By default, a\n",
" | ``DummyEstimator`` predicting the classes priors is used.\n",
" | \n",
" | random_state : int or RandomState, default=None\n",
" | Controls the random seed given to each Tree estimator at each\n",
" | boosting iteration.\n",
" | In addition, it controls the random permutation of the features at\n",
" | each split (see Notes for more details).\n",
" | It also controls the random spliting of the training data to obtain a\n",
" | validation set if `n_iter_no_change` is not None.\n",
" | Pass an int for reproducible output across multiple function calls.\n",
" | See :term:`Glossary <random_state>`.\n",
" | \n",
" | max_features : {'auto', 'sqrt', 'log2'}, int or float, default=None\n",
" | The number of features to consider when looking for the best split:\n",
" | \n",
" | - If int, then consider `max_features` features at each split.\n",
" | - If float, then `max_features` is a fraction and\n",
" | `int(max_features * n_features)` features are considered at each\n",
" | split.\n",
" | - If 'auto', then `max_features=sqrt(n_features)`.\n",
" | - If 'sqrt', then `max_features=sqrt(n_features)`.\n",
" | - If 'log2', then `max_features=log2(n_features)`.\n",
" | - If None, then `max_features=n_features`.\n",
" | \n",
" | Choosing `max_features < n_features` leads to a reduction of variance\n",
" | and an increase in bias.\n",
" | \n",
" | Note: the search for a split does not stop until at least one\n",
" | valid partition of the node samples is found, even if it requires to\n",
" | effectively inspect more than ``max_features`` features.\n",
" | \n",
" | verbose : int, default=0\n",
" | Enable verbose output. If 1 then it prints progress and performance\n",
" | once in a while (the more trees the lower the frequency). If greater\n",
" | than 1 then it prints progress and performance for every tree.\n",
" | \n",
" | max_leaf_nodes : int, default=None\n",
" | Grow trees with ``max_leaf_nodes`` in best-first fashion.\n",
" | Best nodes are defined as relative reduction in impurity.\n",
" | If None then unlimited number of leaf nodes.\n",
" | \n",
" | warm_start : bool, default=False\n",
" | When set to ``True``, reuse the solution of the previous call to fit\n",
" | and add more estimators to the ensemble, otherwise, just erase the\n",
" | previous solution. See :term:`the Glossary <warm_start>`.\n",
" | \n",
" | presort : deprecated, default='deprecated'\n",
" | This parameter is deprecated and will be removed in v0.24.\n",
" | \n",
" | .. deprecated :: 0.22\n",
" | \n",
" | validation_fraction : float, default=0.1\n",
" | The proportion of training data to set aside as validation set for\n",
" | early stopping. Must be between 0 and 1.\n",
" | Only used if ``n_iter_no_change`` is set to an integer.\n",
" | \n",
" | .. versionadded:: 0.20\n",
" | \n",
" | n_iter_no_change : int, default=None\n",
" | ``n_iter_no_change`` is used to decide if early stopping will be used\n",
" | to terminate training when validation score is not improving. By\n",
" | default it is set to None to disable early stopping. If set to a\n",
" | number, it will set aside ``validation_fraction`` size of the training\n",
" | data as validation and terminate training when validation score is not\n",
" | improving in all of the previous ``n_iter_no_change`` numbers of\n",
" | iterations. The split is stratified.\n",
" | \n",
" | .. versionadded:: 0.20\n",
" | \n",
" | tol : float, default=1e-4\n",
" | Tolerance for the early stopping. When the loss is not improving\n",
" | by at least tol for ``n_iter_no_change`` iterations (if set to a\n",
" | number), the training stops.\n",
" | \n",
" | .. versionadded:: 0.20\n",
" | \n",
" | ccp_alpha : non-negative float, default=0.0\n",
" | Complexity parameter used for Minimal Cost-Complexity Pruning. The\n",
" | subtree with the largest cost complexity that is smaller than\n",
" | ``ccp_alpha`` will be chosen. By default, no pruning is performed. See\n",
" | :ref:`minimal_cost_complexity_pruning` for details.\n",
" | \n",
" | .. versionadded:: 0.22\n",
" | \n",
" | Attributes\n",
" | ----------\n",
" | n_estimators_ : int\n",
" | The number of estimators as selected by early stopping (if\n",
" | ``n_iter_no_change`` is specified). Otherwise it is set to\n",
" | ``n_estimators``.\n",
" | \n",
" | .. versionadded:: 0.20\n",
" | \n",
" | feature_importances_ : ndarray of shape (n_features,)\n",
" | The impurity-based feature importances.\n",
" | The higher, the more important the feature.\n",
" | The importance of a feature is computed as the (normalized)\n",
" | total reduction of the criterion brought by that feature. It is also\n",
" | known as the Gini importance.\n",
" | \n",
" | Warning: impurity-based feature importances can be misleading for\n",
" | high cardinality features (many unique values). See\n",
" | :func:`sklearn.inspection.permutation_importance` as an alternative.\n",
" | \n",
" | oob_improvement_ : ndarray of shape (n_estimators,)\n",
" | The improvement in loss (= deviance) on the out-of-bag samples\n",
" | relative to the previous iteration.\n",
" | ``oob_improvement_[0]`` is the improvement in\n",
" | loss of the first stage over the ``init`` estimator.\n",
" | Only available if ``subsample < 1.0``\n",
" | \n",
" | train_score_ : ndarray of shape (n_estimators,)\n",
" | The i-th score ``train_score_[i]`` is the deviance (= loss) of the\n",
" | model at iteration ``i`` on the in-bag sample.\n",
" | If ``subsample == 1`` this is the deviance on the training data.\n",
" | \n",
" | loss_ : LossFunction\n",
" | The concrete ``LossFunction`` object.\n",
" | \n",
" | init_ : estimator\n",
" | The estimator that provides the initial predictions.\n",
" | Set via the ``init`` argument or ``loss.init_estimator``.\n",
" | \n",
" | estimators_ : ndarray of DecisionTreeRegressor of shape (n_estimators, ``loss_.K``)\n",
" | The collection of fitted sub-estimators. ``loss_.K`` is 1 for binary\n",
" | classification, otherwise n_classes.\n",
" | \n",
" | classes_ : ndarray of shape (n_classes,)\n",
" | The classes labels.\n",
" | \n",
" | n_features_ : int\n",
" | The number of data features.\n",
" | \n",
" | n_classes_ : int\n",
" | The number of classes.\n",
" | \n",
" | max_features_ : int\n",
" | The inferred value of max_features.\n",
" | \n",
" | Notes\n",
" | -----\n",
" | The features are always randomly permuted at each split. Therefore,\n",
" | the best found split may vary, even with the same training data and\n",
" | ``max_features=n_features``, if the improvement of the criterion is\n",
" | identical for several splits enumerated during the search of the best\n",
" | split. To obtain a deterministic behaviour during fitting,\n",
" | ``random_state`` has to be fixed.\n",
" | \n",
" | Examples\n",
" | --------\n",
" | >>> from sklearn.datasets import make_classification\n",
" | >>> from sklearn.ensemble import GradientBoostingClassifier\n",
" | >>> from sklearn.model_selection import train_test_split\n",
" | >>> X, y = make_classification(random_state=0)\n",
" | >>> X_train, X_test, y_train, y_test = train_test_split(\n",
" | ... X, y, random_state=0)\n",
" | >>> clf = GradientBoostingClassifier(random_state=0)\n",
" | >>> clf.fit(X_train, y_train)\n",
" | GradientBoostingClassifier(random_state=0)\n",
" | >>> clf.predict(X_test[:2])\n",
" | array([1, 0])\n",
" | >>> clf.score(X_test, y_test)\n",
" | 0.88\n",
" | \n",
" | See also\n",
" | --------\n",
" | sklearn.ensemble.HistGradientBoostingClassifier,\n",
" | sklearn.tree.DecisionTreeClassifier, RandomForestClassifier\n",
" | AdaBoostClassifier\n",
" | \n",
" | References\n",
" | ----------\n",
" | J. Friedman, Greedy Function Approximation: A Gradient Boosting\n",
" | Machine, The Annals of Statistics, Vol. 29, No. 5, 2001.\n",
" | \n",
" | J. Friedman, Stochastic Gradient Boosting, 1999\n",
" | \n",
" | T. Hastie, R. Tibshirani and J. Friedman.\n",
" | Elements of Statistical Learning Ed. 2, Springer, 2009.\n",
" | \n",
" | Method resolution order:\n",
" | GradientBoostingClassifier\n",
" | sklearn.base.ClassifierMixin\n",
" | BaseGradientBoosting\n",
" | sklearn.ensemble._base.BaseEnsemble\n",
" | sklearn.base.MetaEstimatorMixin\n",
" | sklearn.base.BaseEstimator\n",
" | builtins.object\n",
" | \n",
" | Methods defined here:\n",
" | \n",
" | __init__(self, *, loss='deviance', learning_rate=0.1, n_estimators=100, subsample=1.0, criterion='friedman_mse', min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_depth=3, min_impurity_decrease=0.0, min_impurity_split=None, init=None, random_state=None, max_features=None, verbose=0, max_leaf_nodes=None, warm_start=False, presort='deprecated', validation_fraction=0.1, n_iter_no_change=None, tol=0.0001, ccp_alpha=0.0)\n",
" | Initialize self. See help(type(self)) for accurate signature.\n",
" | \n",
" | decision_function(self, X)\n",
" | Compute the decision function of ``X``.\n",
" | \n",
" | Parameters\n",
" | ----------\n",
" | X : {array-like, sparse matrix} of shape (n_samples, n_features)\n",
" | The input samples. Internally, it will be converted to\n",
" | ``dtype=np.float32`` and if a sparse matrix is provided\n",
" | to a sparse ``csr_matrix``.\n",
" | \n",
" | Returns\n",
" | -------\n",
" | score : ndarray of shape (n_samples, n_classes) or (n_samples,)\n",
" | The decision function of the input samples, which corresponds to\n",
" | the raw values predicted from the trees of the ensemble . The\n",
" | order of the classes corresponds to that in the attribute\n",
" | :term:`classes_`. Regression and binary classification produce an\n",
" | array of shape [n_samples].\n",
" | \n",
" | predict(self, X)\n",
" | Predict class for X.\n",
" | \n",
" | Parameters\n",
" | ----------\n",
" | X : {array-like, sparse matrix} of shape (n_samples, n_features)\n",
" | The input samples. Internally, it will be converted to\n",
" | ``dtype=np.float32`` and if a sparse matrix is provided\n",
" | to a sparse ``csr_matrix``.\n",
" | \n",
" | Returns\n",
" | -------\n",
" | y : ndarray of shape (n_samples,)\n",
" | The predicted values.\n",
" | \n",
" | predict_log_proba(self, X)\n",
" | Predict class log-probabilities for X.\n",
" | \n",
" | Parameters\n",
" | ----------\n",
" | X : {array-like, sparse matrix} of shape (n_samples, n_features)\n",
" | The input samples. Internally, it will be converted to\n",
" | ``dtype=np.float32`` and if a sparse matrix is provided\n",
" | to a sparse ``csr_matrix``.\n",
" | \n",
" | Raises\n",
" | ------\n",
" | AttributeError\n",
" | If the ``loss`` does not support probabilities.\n",
" | \n",
" | Returns\n",
" | -------\n",
" | p : ndarray of shape (n_samples, n_classes)\n",
" | The class log-probabilities of the input samples. The order of the\n",
" | classes corresponds to that in the attribute :term:`classes_`.\n",
" | \n",
" | predict_proba(self, X)\n",
" | Predict class probabilities for X.\n",
" | \n",
" | Parameters\n",
" | ----------\n",
" | X : {array-like, sparse matrix} of shape (n_samples, n_features)\n",
" | The input samples. Internally, it will be converted to\n",
" | ``dtype=np.float32`` and if a sparse matrix is provided\n",
" | to a sparse ``csr_matrix``.\n",
" | \n",
" | Raises\n",
" | ------\n",
" | AttributeError\n",
" | If the ``loss`` does not support probabilities.\n",
" | \n",
" | Returns\n",
" | -------\n",
" | p : ndarray of shape (n_samples, n_classes)\n",
" | The class probabilities of the input samples. The order of the\n",
" | classes corresponds to that in the attribute :term:`classes_`.\n",
" | \n",
" | staged_decision_function(self, X)\n",
" | Compute decision function of ``X`` for each iteration.\n",
" | \n",
" | This method allows monitoring (i.e. determine error on testing set)\n",
" | after each stage.\n",
" | \n",
" | Parameters\n",
" | ----------\n",
" | X : {array-like, sparse matrix} of shape (n_samples, n_features)\n",
" | The input samples. Internally, it will be converted to\n",
" | ``dtype=np.float32`` and if a sparse matrix is provided\n",
" | to a sparse ``csr_matrix``.\n",
" | \n",
" | Returns\n",
" | -------\n",
" | score : generator of ndarray of shape (n_samples, k)\n",
" | The decision function of the input samples, which corresponds to\n",
" | the raw values predicted from the trees of the ensemble . The\n",
" | classes corresponds to that in the attribute :term:`classes_`.\n",
" | Regression and binary classification are special cases with\n",
" | ``k == 1``, otherwise ``k==n_classes``.\n",
" | \n",
" | staged_predict(self, X)\n",
" | Predict class at each stage for X.\n",
" | \n",
" | This method allows monitoring (i.e. determine error on testing set)\n",
" | after each stage.\n",
" | \n",
" | Parameters\n",
" | ----------\n",
" | X : {array-like, sparse matrix} of shape (n_samples, n_features)\n",
" | The input samples. Internally, it will be converted to\n",
" | ``dtype=np.float32`` and if a sparse matrix is provided\n",
" | to a sparse ``csr_matrix``.\n",
" | \n",
" | Returns\n",
" | -------\n",
" | y : generator of ndarray of shape (n_samples,)\n",
" | The predicted value of the input samples.\n",
" | \n",
" | staged_predict_proba(self, X)\n",
" | Predict class probabilities at each stage for X.\n",
" | \n",
" | This method allows monitoring (i.e. determine error on testing set)\n",
" | after each stage.\n",
" | \n",
" | Parameters\n",
" | ----------\n",
" | X : {array-like, sparse matrix} of shape (n_samples, n_features)\n",
" | The input samples. Internally, it will be converted to\n",
" | ``dtype=np.float32`` and if a sparse matrix is provided\n",
" | to a sparse ``csr_matrix``.\n",
" | \n",
" | Returns\n",
" | -------\n",
" | y : generator of ndarray of shape (n_samples,)\n",
" | The predicted value of the input samples.\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Data and other attributes defined here:\n",
" | \n",
" | __abstractmethods__ = frozenset()\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Methods inherited from sklearn.base.ClassifierMixin:\n",
" | \n",
" | score(self, X, y, sample_weight=None)\n",
" | Return the mean accuracy on the given test data and labels.\n",
" | \n",
" | In multi-label classification, this is the subset accuracy\n",
" | which is a harsh metric since you require for each sample that\n",
" | each label set be correctly predicted.\n",
" | \n",
" | Parameters\n",
" | ----------\n",
" | X : array-like of shape (n_samples, n_features)\n",
" | Test samples.\n",
" | \n",
" | y : array-like of shape (n_samples,) or (n_samples, n_outputs)\n",
" | True labels for X.\n",
" | \n",
" | sample_weight : array-like of shape (n_samples,), default=None\n",
" | Sample weights.\n",
" | \n",
" | Returns\n",
" | -------\n",
" | score : float\n",
" | Mean accuracy of self.predict(X) wrt. y.\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Data descriptors inherited from sklearn.base.ClassifierMixin:\n",
" | \n",
" | __dict__\n",
" | dictionary for instance variables (if defined)\n",
" | \n",
" | __weakref__\n",
" | list of weak references to the object (if defined)\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Methods inherited from BaseGradientBoosting:\n",
" | \n",
" | apply(self, X)\n",
" | Apply trees in the ensemble to X, return leaf indices.\n",
" | \n",
" | .. versionadded:: 0.17\n",
" | \n",
" | Parameters\n",
" | ----------\n",
" | X : {array-like, sparse matrix} of shape (n_samples, n_features)\n",
" | The input samples. Internally, its dtype will be converted to\n",
" | ``dtype=np.float32``. If a sparse matrix is provided, it will\n",
" | be converted to a sparse ``csr_matrix``.\n",
" | \n",
" | Returns\n",
" | -------\n",
" | X_leaves : array-like of shape (n_samples, n_estimators, n_classes)\n",
" | For each datapoint x in X and for each tree in the ensemble,\n",
" | return the index of the leaf x ends up in each estimator.\n",
" | In the case of binary classification n_classes is 1.\n",
" | \n",
" | fit(self, X, y, sample_weight=None, monitor=None)\n",
" | Fit the gradient boosting model.\n",
" | \n",
" | Parameters\n",
" | ----------\n",
" | X : {array-like, sparse matrix} of shape (n_samples, n_features)\n",
" | The input samples. Internally, it will be converted to\n",
" | ``dtype=np.float32`` and if a sparse matrix is provided\n",
" | to a sparse ``csr_matrix``.\n",
" | \n",
" | y : array-like of shape (n_samples,)\n",
" | Target values (strings or integers in classification, real numbers\n",
" | in regression)\n",
" | For classification, labels must correspond to classes.\n",
" | \n",
" | sample_weight : array-like of shape (n_samples,), default=None\n",
" | Sample weights. If None, then samples are equally weighted. Splits\n",
" | that would create child nodes with net zero or negative weight are\n",
" | ignored while searching for a split in each node. In the case of\n",
" | classification, splits are also ignored if they would result in any\n",
" | single class carrying a negative weight in either child node.\n",
" | \n",
" | monitor : callable, default=None\n",
" | The monitor is called after each iteration with the current\n",
" | iteration, a reference to the estimator and the local variables of\n",
" | ``_fit_stages`` as keyword arguments ``callable(i, self,\n",
" | locals())``. If the callable returns ``True`` the fitting procedure\n",
" | is stopped. The monitor can be used for various things such as\n",
" | computing held-out estimates, early stopping, model introspect, and\n",
" | snapshoting.\n",
" | \n",
" | Returns\n",
" | -------\n",
" | self : object\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Readonly properties inherited from BaseGradientBoosting:\n",
" | \n",
" | feature_importances_\n",
" | The impurity-based feature importances.\n",
" | \n",
" | The higher, the more important the feature.\n",
" | The importance of a feature is computed as the (normalized)\n",
" | total reduction of the criterion brought by that feature. It is also\n",
" | known as the Gini importance.\n",
" | \n",
" | Warning: impurity-based feature importances can be misleading for\n",
" | high cardinality features (many unique values). See\n",
" | :func:`sklearn.inspection.permutation_importance` as an alternative.\n",
" | \n",
" | Returns\n",
" | -------\n",
" | feature_importances_ : array, shape (n_features,)\n",
" | The values of this array sum to 1, unless all trees are single node\n",
" | trees consisting of only the root node, in which case it will be an\n",
" | array of zeros.\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Methods inherited from sklearn.ensemble._base.BaseEnsemble:\n",
" | \n",
" | __getitem__(self, index)\n",
" | Return the index'th estimator in the ensemble.\n",
" | \n",
" | __iter__(self)\n",
" | Return iterator over estimators in the ensemble.\n",
" | \n",
" | __len__(self)\n",
" | Return the number of estimators in the ensemble.\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Data and other attributes inherited from sklearn.ensemble._base.BaseEnsemble:\n",
" | \n",
" | __annotations__ = {'_required_parameters': typing.List[str]}\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Methods inherited from sklearn.base.BaseEstimator:\n",
" | \n",
" | __getstate__(self)\n",
" | \n",
" | __repr__(self, N_CHAR_MAX=700)\n",
" | Return repr(self).\n",
" | \n",
" | __setstate__(self, state)\n",
" | \n",
" | get_params(self, deep=True)\n",
" | Get parameters for this estimator.\n",
" | \n",
" | Parameters\n",
" | ----------\n",
" | deep : bool, default=True\n",
" | If True, will return the parameters for this estimator and\n",
" | contained subobjects that are estimators.\n",
" | \n",
" | Returns\n",
" | -------\n",
" | params : mapping of string to any\n",
" | Parameter names mapped to their values.\n",
" | \n",
" | set_params(self, **params)\n",
" | Set the parameters of this estimator.\n",
" | \n",
" | The method works on simple estimators as well as on nested objects\n",
" | (such as pipelines). The latter have parameters of the form\n",
" | ``<component>__<parameter>`` so that it's possible to update each\n",
" | component of a nested object.\n",
" | \n",
" | Parameters\n",
" | ----------\n",
" | **params : dict\n",
" | Estimator parameters.\n",
" | \n",
" | Returns\n",
" | -------\n",
" | self : object\n",
" | Estimator instance.\n",
"\n"
]
}
],
"source": [
"help(GradientBoostingClassifier)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import GridSearchCV"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"param_grid = {\"n_estimators\":[1,5,10,20,40,100],'max_depth':[3,4,5,6]}"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"gb_model = GradientBoostingClassifier()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"grid = GridSearchCV(gb_model,param_grid)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Fit to Training Data with CV Search"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"GridSearchCV(estimator=GradientBoostingClassifier(),\n",
" param_grid={'max_depth': [3, 4, 5, 6],\n",
" 'n_estimators': [1, 5, 10, 20, 40, 100]})"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grid.fit(X_train,y_train)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'max_depth': 3, 'n_estimators': 100}"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grid.best_params_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Performance "
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import classification_report,plot_confusion_matrix,accuracy_score"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"predictions = grid.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['p', 'e', 'p', ..., 'p', 'p', 'e'], dtype=object)"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"predictions"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" e 1.00 1.00 1.00 655\n",
" p 1.00 1.00 1.00 564\n",
"\n",
" accuracy 1.00 1219\n",
" macro avg 1.00 1.00 1.00 1219\n",
"weighted avg 1.00 1.00 1.00 1219\n",
"\n"
]
}
],
"source": [
"print(classification_report(y_test,predictions))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([2.91150176e-04, 1.55427847e-17, 2.67658844e-21, 0.00000000e+00,\n",
" 1.11459235e-16, 1.05030313e-03, 3.26837862e-18, 9.23288948e-17,\n",
" 3.33934930e-18, 0.00000000e+00, 1.27133255e-17, 0.00000000e+00,\n",
" 3.56629935e-17, 2.46527883e-21, 0.00000000e+00, 5.60405971e-07,\n",
" 2.31055039e-03, 5.13955090e-02, 1.84253604e-04, 1.40371481e-02,\n",
" 1.82499853e-02, 3.13472494e-03, 6.14744334e-01, 9.20844491e-04,\n",
" 0.00000000e+00, 0.00000000e+00, 5.26020065e-19, 1.25278108e-02,\n",
" 1.16509070e-02, 0.00000000e+00, 4.86322971e-17, 0.00000000e+00,\n",
" 1.08107877e-17, 1.10350576e-21, 0.00000000e+00, 3.66548515e-17,\n",
" 2.07693543e-16, 0.00000000e+00, 1.80938787e-17, 0.00000000e+00,\n",
" 4.39922283e-04, 0.00000000e+00, 1.35977416e-01, 7.71855052e-03,\n",
" 3.23882633e-02, 4.64723214e-04, 1.49599812e-03, 4.95063766e-06,\n",
" 1.83319493e-05, 1.70638552e-06, 3.38601933e-02, 2.07732168e-03,\n",
" 0.00000000e+00, 0.00000000e+00, 6.80156959e-04, 0.00000000e+00,\n",
" 0.00000000e+00, 5.74694069e-04, 0.00000000e+00, 1.84524355e-04,\n",
" 0.00000000e+00, 0.00000000e+00, 5.33104127e-05, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 3.02342639e-03, 0.00000000e+00,\n",
" 1.35380870e-07, 7.74443653e-05, 2.62871992e-03, 0.00000000e+00,\n",
" 7.08716926e-05, 0.00000000e+00, 1.32788040e-03, 1.83494013e-03,\n",
" 7.34476580e-03, 2.14240381e-04, 2.08941481e-04, 0.00000000e+00,\n",
" 3.04953583e-02, 4.10000880e-03, 4.86768755e-04, 0.00000000e+00,\n",
" 1.17434515e-03, 0.00000000e+00, 7.67619495e-08, 1.34881889e-05,\n",
" 5.50395653e-04, 1.82220979e-16, 0.00000000e+00, 9.07373812e-17,\n",
" 0.00000000e+00, 1.00485103e-05, 0.00000000e+00])"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grid.best_estimator_.feature_importances_"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"feat_import = grid.best_estimator_.feature_importances_"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"imp_feats = pd.DataFrame(index=X.columns,data=feat_import,columns=['Importance'])"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Importance</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>cap-shape_c</th>\n",
" <td>2.911502e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cap-shape_f</th>\n",
" <td>1.554278e-17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cap-shape_k</th>\n",
" <td>2.676588e-21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cap-shape_s</th>\n",
" <td>0.000000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cap-shape_x</th>\n",
" <td>1.114592e-16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>habitat_l</th>\n",
" <td>0.000000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>habitat_m</th>\n",
" <td>9.073738e-17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>habitat_p</th>\n",
" <td>0.000000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>habitat_u</th>\n",
" <td>1.004851e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>habitat_w</th>\n",
" <td>0.000000e+00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>95 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" Importance\n",
"cap-shape_c 2.911502e-04\n",
"cap-shape_f 1.554278e-17\n",
"cap-shape_k 2.676588e-21\n",
"cap-shape_s 0.000000e+00\n",
"cap-shape_x 1.114592e-16\n",
"... ...\n",
"habitat_l 0.000000e+00\n",
"habitat_m 9.073738e-17\n",
"habitat_p 0.000000e+00\n",
"habitat_u 1.004851e-05\n",
"habitat_w 0.000000e+00\n",
"\n",
"[95 rows x 1 columns]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"imp_feats"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Importance</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>odor_n</th>\n",
" <td>0.614744</td>\n",
" </tr>\n",
" <tr>\n",
" <th>stalk-root_c</th>\n",
" <td>0.135977</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bruises_t</th>\n",
" <td>0.051396</td>\n",
" </tr>\n",
" <tr>\n",
" <th>stalk-surface-below-ring_y</th>\n",
" <td>0.033860</td>\n",
" </tr>\n",
" <tr>\n",
" <th>stalk-root_r</th>\n",
" <td>0.032388</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>veil-color_o</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gill-color_y</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>odor_y</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>odor_s</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>habitat_w</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>95 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" Importance\n",
"odor_n 0.614744\n",
"stalk-root_c 0.135977\n",
"bruises_t 0.051396\n",
"stalk-surface-below-ring_y 0.033860\n",
"stalk-root_r 0.032388\n",
"... ...\n",
"veil-color_o 0.000000\n",
"gill-color_y 0.000000\n",
"odor_y 0.000000\n",
"odor_s 0.000000\n",
"habitat_w 0.000000\n",
"\n",
"[95 rows x 1 columns]"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"imp_feats.sort_values(\"Importance\",ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Importance</th>\n",
" <td>95.0</td>\n",
" <td>0.010526</td>\n",
" <td>0.06463</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.822210e-16</td>\n",
" <td>0.000801</td>\n",
" <td>0.614744</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count mean std min 25% 50% 75% \\\n",
"Importance 95.0 0.010526 0.06463 0.0 0.0 1.822210e-16 0.000801 \n",
"\n",
" max \n",
"Importance 0.614744 "
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"imp_feats.describe().transpose()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"imp_feats = imp_feats[imp_feats['Importance'] > 0.000527]"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Importance</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>population_y</th>\n",
" <td>0.000550</td>\n",
" </tr>\n",
" <tr>\n",
" <th>stalk-color-above-ring_w</th>\n",
" <td>0.000575</td>\n",
" </tr>\n",
" <tr>\n",
" <th>stalk-color-above-ring_n</th>\n",
" <td>0.000680</td>\n",
" </tr>\n",
" <tr>\n",
" <th>odor_p</th>\n",
" <td>0.000921</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cap-surface_g</th>\n",
" <td>0.001050</td>\n",
" </tr>\n",
" <tr>\n",
" <th>population_c</th>\n",
" <td>0.001174</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ring-type_n</th>\n",
" <td>0.001328</td>\n",
" </tr>\n",
" <tr>\n",
" <th>stalk-surface-above-ring_s</th>\n",
" <td>0.001496</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ring-type_p</th>\n",
" <td>0.001835</td>\n",
" </tr>\n",
" <tr>\n",
" <th>stalk-color-above-ring_c</th>\n",
" <td>0.002077</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cap-color_y</th>\n",
" <td>0.002311</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ring-number_o</th>\n",
" <td>0.002629</td>\n",
" </tr>\n",
" <tr>\n",
" <th>stalk-color-below-ring_y</th>\n",
" <td>0.003023</td>\n",
" </tr>\n",
" <tr>\n",
" <th>odor_m</th>\n",
" <td>0.003135</td>\n",
" </tr>\n",
" <tr>\n",
" <th>spore-print-color_u</th>\n",
" <td>0.004100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>spore-print-color_h</th>\n",
" <td>0.007345</td>\n",
" </tr>\n",
" <tr>\n",
" <th>stalk-root_e</th>\n",
" <td>0.007719</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gill-size_n</th>\n",
" <td>0.011651</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gill-spacing_w</th>\n",
" <td>0.012528</td>\n",
" </tr>\n",
" <tr>\n",
" <th>odor_f</th>\n",
" <td>0.014037</td>\n",
" </tr>\n",
" <tr>\n",
" <th>odor_l</th>\n",
" <td>0.018250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>spore-print-color_r</th>\n",
" <td>0.030495</td>\n",
" </tr>\n",
" <tr>\n",
" <th>stalk-root_r</th>\n",
" <td>0.032388</td>\n",
" </tr>\n",
" <tr>\n",
" <th>stalk-surface-below-ring_y</th>\n",
" <td>0.033860</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bruises_t</th>\n",
" <td>0.051396</td>\n",
" </tr>\n",
" <tr>\n",
" <th>stalk-root_c</th>\n",
" <td>0.135977</td>\n",
" </tr>\n",
" <tr>\n",
" <th>odor_n</th>\n",
" <td>0.614744</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Importance\n",
"population_y 0.000550\n",
"stalk-color-above-ring_w 0.000575\n",
"stalk-color-above-ring_n 0.000680\n",
"odor_p 0.000921\n",
"cap-surface_g 0.001050\n",
"population_c 0.001174\n",
"ring-type_n 0.001328\n",
"stalk-surface-above-ring_s 0.001496\n",
"ring-type_p 0.001835\n",
"stalk-color-above-ring_c 0.002077\n",
"cap-color_y 0.002311\n",
"ring-number_o 0.002629\n",
"stalk-color-below-ring_y 0.003023\n",
"odor_m 0.003135\n",
"spore-print-color_u 0.004100\n",
"spore-print-color_h 0.007345\n",
"stalk-root_e 0.007719\n",
"gill-size_n 0.011651\n",
"gill-spacing_w 0.012528\n",
"odor_f 0.014037\n",
"odor_l 0.018250\n",
"spore-print-color_r 0.030495\n",
"stalk-root_r 0.032388\n",
"stalk-surface-below-ring_y 0.033860\n",
"bruises_t 0.051396\n",
"stalk-root_c 0.135977\n",
"odor_n 0.614744"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"imp_feats.sort_values('Importance')"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
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\n",
"text/plain": [
"<Figure size 2800x1200 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(14,6),dpi=200)\n",
"sns.barplot(data=imp_feats.sort_values('Importance'),x=imp_feats.sort_values('Importance').index,y='Importance')\n",
"plt.xticks(rotation=90);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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