from sklearn.model_selection import KFold

# Use KFold
kf = KFold(n_splits=5, shuffle=True, random_state=1111)

# Create splits
splits = kf.split(X)

# Print the number of indices
for train_index, val_index in splits:
    print("Number of training indices: %s" % len(train_index))
    print("Number of validation indices: %s" % len(val_index))

from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error

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

# Access the training and validation indices of splits
for train_index, val_index in splits:
    # Setup the training and validation data
    X_train, y_train = X[train_index], y[train_index]
    X_val, y_val = X[val_index], y[val_index]
    # Fit the random forest model
    rfc.fit(X_train, y_train)
    # Make predictions, and print the accuracy
    predictions = rfc.predict(X_val)
    print("Split accuracy: " + str(mean_squared_error(y_val, predictions)))

5e6dcf74da26fcba680b6233a86ed3f5b256eb27
<mask token>

<mask token>
for train_index, val_index in splits:
    print('Number of training indices: %s' % len(train_index))
    print('Number of validation indices: %s' % len(val_index))
<mask token>
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]
    rfc.fit(X_train, y_train)
    predictions = rfc.predict(X_val)
    print('Split accuracy: ' + str(mean_squared_error(y_val, predictions)))

<mask token>
kf = KFold(n_splits=5, shuffle=True, random_state=1111)
splits = kf.split(X)
for train_index, val_index in splits:
    print('Number of training indices: %s' % len(train_index))
    print('Number of validation indices: %s' % len(val_index))
<mask token>
rfc = RandomForestRegressor(n_estimators=25, random_state=1111)
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]
    rfc.fit(X_train, y_train)
    predictions = rfc.predict(X_val)
    print('Split accuracy: ' + str(mean_squared_error(y_val, predictions)))

from sklearn.model_selection import KFold
kf = KFold(n_splits=5, shuffle=True, random_state=1111)
splits = kf.split(X)
for train_index, val_index in splits:
    print('Number of training indices: %s' % len(train_index))
    print('Number of validation indices: %s' % len(val_index))
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
rfc = RandomForestRegressor(n_estimators=25, random_state=1111)
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]
    rfc.fit(X_train, y_train)
    predictions = rfc.predict(X_val)
    print('Split accuracy: ' + str(mean_squared_error(y_val, predictions)))

from sklearn.model_selection import KFold

# Use KFold
kf = KFold(n_splits=5, shuffle=True, random_state=1111)

# Create splits
splits = kf.split(X)

# Print the number of indices
for train_index, val_index in splits:
    print("Number of training indices: %s" % len(train_index))
    print("Number of validation indices: %s" % len(val_index))

from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error

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

# Access the training and validation indices of splits
for train_index, val_index in splits:
    # Setup the training and validation data
    X_train, y_train = X[train_index], y[train_index]
    X_val, y_val = X[val_index], y[val_index]
    # Fit the random forest model
    rfc.fit(X_train, y_train)
    # Make predictions, and print the accuracy
    predictions = rfc.predict(X_val)
    print("Split accuracy: " + str(mean_squared_error(y_val, predictions)))
