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%pylab inline
pylab.rcParams['figure.figsize'] = (14, 14)
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from sklearn.datasets import load_digits as load_data
from sklearn.naive_bayes import GaussianNB
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# This is all that's needed for scikit-plot
import matplotlib.pyplot as plt
from scikitplot import classifier_factory
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# Load data
X, y = load_data(return_X_y=True)
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# Regular instance using GaussianNB class
nb = GaussianNB()
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# Modification of instance of Scikit-Learn
classifier_factory(nb)
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# An object of Scikit-Learn using the modified version that can use a method plot_roc_curve
nb.plot_roc_curve(X, y, random_state=1)
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# Display plot
plt.show()
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from sklearn.ensemble import RandomForestClassifier
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random_forest_clf = RandomForestClassifier(n_estimators=5, max_depth=5, random_state=1)
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from scikitplot import classifier_factory
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classifier_factory(random_forest_clf)
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random_forest_clf.plot_confusion_matrix(X, y, normalize=True)
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plt.show()
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from scikitplot import plotters as skplt
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rf = RandomForestClassifier()
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rf = rf.fit(X, y)
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preds = rf.predict(X)
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skplt.plot_confusion_matrix(y_true=y, y_pred=preds)
plt.show()
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from __future__ import absolute_import
import matplotlib.pyplot as plt
from scikitplot import clustering_factory
from sklearn.cluster import KMeans
from sklearn.datasets import load_iris as load_data
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X, y = load_data(return_X_y=True)
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kmeans = clustering_factory(KMeans(random_state=1))
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kmeans.plot_elbow_curve(X, cluster_ranges=range(1, 11))
plt.show()
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from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris as load_data
import matplotlib.pyplot as plt
from scikitplot import classifier_factory
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X, y = load_data(return_X_y=True)
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rf = classifier_factory(RandomForestClassifier(random_state=1))
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rf.fit(X, y)
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rf.plot_feature_importances(feature_names=['petal length', 'petal width',
'sepal length', 'sepal width'])
plt.show()
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from __future__ import absolute_import
import matplotlib.pyplot as plt
from scikitplot import classifier_factory
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_breast_cancer as load_data
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X, y = load_data(return_X_y=True)
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lr = classifier_factory(LogisticRegression())
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lr.plot_ks_statistic(X, y, random_state=1)
plt.show()
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from scikitplot import plotters as skplt
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lr = LogisticRegression()
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lr = lr.fit(X, y)
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probas = lr.predict_proba(X)
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skplt.plot_ks_statistic(y_true=y, y_probas=probas)
plt.show()
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from __future__ import absolute_import
import matplotlib.pyplot as plt
from scikitplot import classifier_factory
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_breast_cancer as load_data
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X, y = load_data(return_X_y=True)
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rf = classifier_factory(RandomForestClassifier())
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rf.plot_learning_curve(X, y)
plt.show()
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from scikitplot import plotters as skplt
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rf = RandomForestClassifier()
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skplt.plot_learning_curve(rf, X, y)
plt.show()
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from sklearn.decomposition import PCA
from sklearn.datasets import load_digits as load_data
import scikitplot.plotters as skplt
import matplotlib.pyplot as plt
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X, y = load_data(return_X_y=True)
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pca = PCA(random_state=1)
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pca.fit(X)
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skplt.plot_pca_2d_projection(pca, X, y)
plt.show()
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from sklearn.decomposition import PCA
from sklearn.datasets import load_digits as load_data
import scikitplot.plotters as skplt
import matplotlib.pyplot as plt
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X, y = load_data(return_X_y=True)
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pca = PCA(random_state=1)
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pca.fit(X)
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skplt.plot_pca_component_variance(pca)
plt.show()
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from __future__ import absolute_import
import matplotlib.pyplot as plt
from scikitplot import classifier_factory
from sklearn.naive_bayes import GaussianNB
from sklearn.datasets import load_digits as load_data
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X, y = load_data(return_X_y=True)
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nb = classifier_factory(GaussianNB())
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nb.plot_precision_recall_curve(X, y, random_state=1)
plt.show()
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from __future__ import absolute_import
import matplotlib.pyplot as plt
from scikitplot import classifier_factory
from sklearn.naive_bayes import GaussianNB
from sklearn.datasets import load_digits as load_data
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X, y = load_data(return_X_y=True)
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nb = classifier_factory(GaussianNB())
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nb.plot_roc_curve(X, y, random_state=1)
plt.show()
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from scikitplot import plotters as skplt
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nb = GaussianNB()
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nb = nb.fit(X, y)
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probas = nb.predict_proba(X)
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skplt.plot_roc_curve(y_true=y, y_probas=probas)
plt.show()
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from __future__ import absolute_import
import matplotlib.pyplot as plt
from scikitplot import clustering_factory
from sklearn.cluster import KMeans
from sklearn.datasets import load_iris as load_data
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X, y = load_data(return_X_y=True)
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kmeans = clustering_factory(KMeans(n_clusters=4, random_state=1))
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kmeans.plot_silhouette(X)
plt.show()
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