clf.fit(x_sm, y_sm)
y_pred = clf.predict(x_test)
train_pred = clf.predict(x_sm)
## train metrics
print("accuracy:", metrics.accuracy_score(y_sm, train_pred))
print("recall:", metrics.recall_score(y_sm, train_pred, pos_label=1))
print("precision:", metrics.precision_score(y_sm, train_pred, pos_label=1))
print("f1-score:", metrics.f1_score(y_sm, train_pred, pos_label=1))
print("======Classification report========")
print(metrics.classification_report(y_sm, train_pred))
plot_confusion_matrix(y_sm, train_pred, classes=[0, 1])
plt.show()
probs = clf.predict_proba(x_sm)[:, 1]
plot_roc_curve(y_sm, probs, pos_label=1)
## test metrics
print("accuracy:", metrics.accuracy_score(y_test, y_pred))
print("recall:", metrics.recall_score(y_test, y_pred, pos_label=1))
print("precision:", metrics.precision_score(y_test, y_pred, pos_label=1))
print("f1-score:", metrics.f1_score(y_test, y_pred, pos_label=1))
print("======Classification report========")
print(metrics.classification_report(y_test, y_pred))
plot_confusion_matrix(y_test, y_pred, classes=[0, 1])
plt.show()
probs = clf.predict_proba(x_test)[:, 1]
plot_roc_curve(y_test, probs, pos_label=1)
## train metrics
print("accuracy:", metrics.accuracy_score(y_train, train_pred))
print("recall:", metrics.recall_score(y_train, train_pred,
pos_label="Success"))
print("precision:",
metrics.precision_score(y_train, train_pred, pos_label="Success"))
print("f1-score:", metrics.f1_score(y_train, train_pred, pos_label="Success"))
print("======Classification report========")
print(metrics.classification_report(y_train, train_pred))
plot_confusion_matrix(y_train, train_pred, classes=["Failure", "Success"])
plt.show()
y_score = logisticRegr.decision_function(x_train)
plot_roc_curve(y_train, y_score)
## test metrics
print("accuracy:", metrics.accuracy_score(y_test, y_pred))
print("recall:", metrics.recall_score(y_test, y_pred, pos_label="Success"))
print("precision:", metrics.precision_score(y_test,
y_pred,
pos_label="Success"))
print("f1-score:", metrics.f1_score(y_test, y_pred, pos_label="Success"))
print("======Classification report========")
print(metrics.classification_report(y_test, y_pred))
plot_confusion_matrix(y_test, y_pred, classes=["Failure", "Success"])
plt.show()
y_score = logisticRegr.decision_function(x_test)
## train metrics
print("accuracy:", metrics.accuracy_score(y_train, train_pred))
print("recall:", metrics.recall_score(y_train, train_pred,
pos_label="Success"))
print("precision:",
metrics.precision_score(y_train, train_pred, pos_label="Success"))
print("f1-score:", metrics.f1_score(y_train, train_pred, pos_label="Success"))
print("======Classification report========")
print(metrics.classification_report(y_train, train_pred))
plot_confusion_matrix(y_train, train_pred, classes=["Failure", "Success"])
plt.show()
probs = clf.predict_proba(x_train)[:, 1]
plot_roc_curve(y_train, probs)
## test metrics
print("accuracy:", metrics.accuracy_score(y_test, y_pred))
print("recall:", metrics.recall_score(y_test, y_pred, pos_label="Success"))
print("precision:", metrics.precision_score(y_test,
y_pred,
pos_label="Success"))
print("f1-score:", metrics.f1_score(y_test, y_pred, pos_label="Success"))
print("======Classification report========")
print(metrics.classification_report(y_test, y_pred))
plot_confusion_matrix(y_test, y_pred, classes=["Failure", "Success"])
plt.show()
probs = clf.predict_proba(x_test)[:, 1]