README.md

1 Confusion Matrix

from sklearn.metrics import confusion_matrix
confusion_matrix(y_train, y_prediction)

	Predict(Positive)	Predict(Negative)
Actual(Positive)	TP(True Positive)	FP(False Positive)
Actual(Negative)	FN(False Negative)	TN(True Negative)

• Precision

$$precision=\frac{TP}{TP+FP}$$

• Recall

$$recall=\frac{TP}{TP+FN}$$

from sklearn.metrics import precision_score, recall_score
precision_score(y_train, y_pred)
recall_score(y_train, y_pred)

• F1 Scores

$$f_1=\frac{2}{\frac{1}{precision}+\frac{1}{recall}}$$

2 Decision Score

from sklearn.model_selection import cross_val_predict
y_scores = cross_val_predict(sgd_clf, X_train, y_train, cv=3, method='decision_function')
# or
y_scores = cross_val_predict(forest_clf, X_train, y_train, cv=3, method='predict_proba')

3 Roc Curve

from sklearn.metrics import roc_curve, roc_auc_score
fpr, tpr, threshold = roc_curve(y_train, y_predict)
roc_auc_score(y_train, y_predict)

4 Multilabel or Multioutput Classification

y_train_large = (y_train >= 7)
y_train_odd = (y_train == 1)
y_multilabel = np.c_[y_train_large, y_train_odd]
forest_clf.fit(X_train, y_mulitlabel)