ensemble penguins classification added

_episodes/ensemble_classification.md

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+## Stacking: classification
+import seaborn as sns
+penguins = sns.load_dataset('penguins')
+
+feature_names = ['bill_length_mm', 'bill_depth_mm', 'flipper_length_mm', 'body_mass_g']
+penguins.dropna(subset=feature_names, inplace=True)
+
+species_names = penguins['species'].unique()
+
+# Define data and targets
+X = penguins[feature_names]
+
+y = penguins.species
+
+# Split data in training and test set
+from sklearn.model_selection import train_test_split
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=5)
+
+print(f'train size: {X_train.shape}')
+print(f'test size: {X_test.shape}')
+
+from sklearn.ensemble import (
+    GradientBoostingClassifier,
+    RandomForestClassifier,
+    VotingClassifier,
+)
+from sklearn.gaussian_process import GaussianProcessClassifier
+from sklearn.gaussian_process.kernels import RBF
+from sklearn.tree import DecisionTreeClassifier
+
+# training estimators 
+rf_clf = RandomForestClassifier(n_estimators=100, max_depth=7, min_samples_leaf=1, random_state=5)
+gb_clf = GradientBoostingClassifier(random_state=5)
+gp_clf = GaussianProcessClassifier(1.0 * RBF(1.0), random_state=5)
+dt_clf = DecisionTreeClassifier(max_depth=5, random_state=5)
+
+voting_reg = VotingClassifier([("rf", rf_clf), ("gb", gb_clf), ("gp", gp_clf), ("dt", dt_clf)])
+
+# fit voting estimator
+voting_reg.fit(X_train, y_train)
+
+# lets also train the individual models for comparison
+rf_clf.fit(X_train, y_train)
+gb_clf.fit(X_train, y_train)
+gp_clf.fit(X_train, y_train)
+dt_clf.fit(X_train, y_train)
+
+import matplotlib.pyplot as plt
+
+# make predictions
+X_test_20 = X_test[:20] # first 20 for visualisation
+
+rf_pred = rf_clf.predict(X_test_20)
+gb_pred = gb_clf.predict(X_test_20)
+gp_pred = gp_clf.predict(X_test_20)
+dt_pred = dt_clf.predict(X_test_20)
+voting_pred = voting_reg.predict(X_test_20)
+
+print(rf_pred)
+print(gb_pred)
+print(gp_pred)
+print(dt_pred)
+print(voting_pred)
+
+plt.figure()
+plt.plot(gb_pred,  "o", color="green", label="GradientBoostingClassifier")
+plt.plot(rf_pred,  "o", color="blue", label="RandomForestClassifier")
+plt.plot(gp_pred,  "o", color="darkblue", label="GuassianProcessClassifier")
+plt.plot(dt_pred,  "o", color="lightblue", label="DecisionTreeClassifier")
+plt.plot(voting_pred,  "x", color="red", ms=10, label="VotingRegressor")
+
+plt.tick_params(axis="x", which="both", bottom=False, top=False, labelbottom=False)
+plt.ylabel("predicted")
+plt.xlabel("training samples")
+plt.legend(loc="best")
+plt.title("Regressor predictions and their average")
+
+plt.show()
+
+print(f'random forest: {rf_clf.score(X_test, y_test)}')
+
+print(f'gradient boost: {gb_clf.score(X_test, y_test)}')
+
+print(f'guassian process: {gp_clf.score(X_test, y_test)}')
+
+print(f'decision tree: {dt_clf.score(X_test, y_test)}')
+
+print(f'voting regressor: {voting_reg.score(X_test, y_test)}')