downsampling

def downsample(features, target, fraction):
    features_zeros = features[target == 0]
    features_ones = features[target == 1]
    target_zeros = target[target == 0]
    target_ones = target[target == 1]

    features_downsampled = pd.concat(
        [features_zeros.sample(frac=fraction, random_state=12345)] + [features_ones])
    target_downsampled = pd.concat(
        [target_zeros.sample(frac=fraction, random_state=12345)] + [target_ones])

    features_downsampled, target_downsampled = shuffle(
        features_downsampled, target_downsampled, random_state=12345)

    return features_downsampled, target_downsampled

features_downsampled, target_downsampled = downsample(features_train, target_train, 0.1)

model = RandomForestClassifier(random_state=12345, criterion='gini', max_depth=11,
                               min_samples_leaf=2, n_estimators=76, class_weight='balanced')

model.fit(features_downsampled, target_downsampled)
predicted_valid = model.predict(features_valid)

print("F1:", f1_score(target_valid, predicted_valid))

#OUTPUT F1: 0.5099236641221373