Python में Model Validation
Kasey Jones
Data Scientist
n_splits: क्रॉस-वैलिडेशन splits की संख्या
n_splits
shuffle: split से पहले डेटा shuffle करना है या नहीं (boolean)
shuffle
random_state: random seed
random_state
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)
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))
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
# किसी एक index सेट को प्रिंट करें: 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))
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
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