3 ways to do dimensional reduction techniques in Scikit-learn
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import pandas as pd | |
from sklearn.linear_model import LogisticRegression | |
from sklearn.model_selection import train_test_split | |
from sklearn.metrics import accuracy_score, f1_score, classification_report | |
from sklearn.datasets import load_breast_cancer | |
(X, y) = load_breast_cancer(return_X_y = True) | |
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) | |
clf = LogisticRegression(random_state=0) | |
clf.fit(X_train, y_train) | |
print('%s: %.3f' % ('Logreg Train Accuracy', accuracy_score(y_train, clf.predict(X_train)))) | |
print('%s: %.3f' % ('Logreg Test Accuracy', accuracy_score(y_test, clf.predict(X_test)))) | |
print('%s: %.3f' % ('Logreg Train F1 Score', f1_score(y_train, clf.predict(X_train)))) | |
print('%s: %.3f' % ('Logreg Test F1 Score', f1_score(y_test, clf.predict(X_test)))) | |
print(classification_report(y_test, clf.predict(X_test))) |
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