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November 19, 2017 17:50
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[PYTHON][SKLEARN] Logistical Regression
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# Import the necessary modules | |
from sklearn.linear_model import LogisticRegression | |
from sklearn.metrics import confusion_matrix, classification_report | |
# Create training and test sets | |
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.4, random_state=42) | |
# Create the classifier: logreg | |
logreg = LogisticRegression() | |
# Fit the classifier to the training data | |
logreg.fit(X_train, y_train) | |
# Predict the labels of the test set: y_pred | |
y_pred = logreg.predict(X_test) | |
# Compute and print the confusion matrix and classification report | |
print(confusion_matrix(y_test, y_pred)) | |
print(classification_report(y_test, y_pred)) |
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