Python में Model Validation
Kasey Jones
Data Scientist
मॉडल वैलिडेशन में शामिल है:
बेसिक मॉडलिंग स्टेप्स:
model = RandomForestRegressor(n_estimators=500, random_state=1111) model.fit(X=X_train, y=y_train)
model = RandomForestRegressor(n_estimators=500, random_state=1111)
model.fit(X=X_train, y=y_train)
RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators=500, n_jobs=1, oob_score=False, random_state=1111, verbose=0, warm_start=False)
predictions = model.predict(X_test) print("{0:.2f}".format(mae(y_true=y_test, y_pred=predictions)))
predictions = model.predict(X_test)
print("{0:.2f}".format(mae(y_true=y_test, y_pred=predictions)))
10.84
मीन एब्सोल्यूट एरर फ़ॉर्मूला
$$ \frac{\sum_{i=1}^{n} |y_i - \hat{y}_i|}{n} $$
ट्रेनिंग डेटा = देखा हुआ डेटा
model = RandomForestRegressor(n_estimators=500, random_state=1111) model.fit(X_train, y_train) train_predictions = model.predict(X_train)
टेस्टिंग डेटा = अनदेखा डेटा
model = RandomForestRegressor(n_estimators=500, random_state=1111) model.fit(X_train, y_train) test_predictions = model.predict(X_test)
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