Cross-validation

Model Validation in Python

Kasey Jones

Data Scientist

Cross-validation

Creating train, validation, and test datasets is completely by splitting your overall data into 3 subsets. An example would be 60% for training, and 20% for both validation and testing.

Cross-validation

Cross-validation requires us to split our data into training and validation splits several times, not just a single 80:20 split. We might use 5 different 80:20 splits.

n_splits: number of cross-validation splits

shuffle: boolean indicating to shuffle data before splitting

random_state: random seed

from sklearn.model_selection import KFold

X = np.array(range(40))
y = np.array([0] * 20 + [1] * 20)

kf = KFold(n_splits=5)

splits = kf.split(X)

kf = KFold(n_splits=5)
splits = kf.split(X)

for train_index, test_index in splits:
    print(len(train_index), len(test_index))

32 8 32 8 32 8 32 8 32 8

# Print one of the index sets:
print(train_index, test_index)

[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 ...]
[32 33 34 35 36 37 38 39]

rfr = RandomForestRegressor(n_estimators=25, random_state=1111)

errors = []
for train_index, val_index in splits:
    X_train, y_train = X[train_index], y[train_index]
    X_val, y_val = X[val_index], y[val_index]

    rfr.fit(X_train, y_train)
    predictions = rfr.predict(X_val)
    errors.append(<some_accuracy_metric>)

print(np.mean(errors))

4.25

Practice time

Model Validation in Python