Confusion matrix diagnostics for threshold selection

                  import numpy as np
from sklearn.metrics import confusion_matrix

probabilities = model.predict_proba(X_valid)[:, 1]
thresholds = np.linspace(0.1, 0.9, 9)

for threshold in thresholds:
    predictions = (probabilities >= threshold).astype(int)
    tn, fp, fn, tp = confusion_matrix(y_valid, predictions).ravel()
    print({
        'threshold': round(float(threshold), 2),
        'tp': int(tp),
        'fp': int(fp),
        'fn': int(fn),
        'tn': int(tn),
    })