betterdatascience
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| lime_exp.as_list() |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| shap.summary_plot(shap_values, X) |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| import shap | |
| shap.initjs() | |
| shap_explainer = shap.TreeExplainer(model) | |
| shap_values = shap_explainer.shap_values(X) | |
| shap.force_plot(shap_explainer.expected_value, shap_values[1, :], test_1) |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| lime_exp.as_list() |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| lime_exp.predict_proba |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| import lime | |
| from lime import lime_tabular | |
| lime_explainer = lime_tabular.LimeTabularExplainer( | |
| training_data=np.array(X_train), | |
| feature_names=X_train.columns, | |
| class_names=['bad', 'good'], | |
| mode='classification' | |
| ) |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| from xgboost import XGBClassifier | |
| model = XGBClassifier() | |
| model.fit(X_train, y_train) | |
| test_1 = X_test.iloc[1] |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| from sklearn.model_selection import train_test_split | |
| X = wine.drop('quality', axis=1) | |
| y = wine['quality'] | |
| X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| import numpy as np | |
| import pandas as pd | |
| wine = pd.read_csv('wine.csv') | |
| wine.head() |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| def generate_color(magnitude): | |
| if magnitude <= 5: | |
| c_outline, c_fill = '#ffda79', '#ffda79' | |
| m_opacity, f_opacity = 0.2, 0.1 | |
| else: | |
| c_outline, c_fill = '#c0392b', '#e74c3c' | |
| m_opacity, f_opacity = 1, 1 | |
| return c_outline, c_fill, m_opacity, f_opacity | |
| def generate_popup(magnitude, depth): |
NewerOlder