Question

I'm confused about using cross cross_val_predict in a test data set.

I created a simple Random Forest model and used cross_val_predict to make predictions

from sklearn.ensemble import RandomForestClassifier
from sklearn.cross_validation import cross_val_predict, KFold

lr = RandomForestClassifier(random_state=1, class_weight="balanced", n_estimators=25, max_depth=6)
kf = KFold(train_df.shape[0], random_state=1)
predictions = cross_val_predict(lr,train_df[features_columns], train_df["target"], cv=kf)
predictions = pd.Series(predictions)

I'm confused on the next step here, How do I use is learnt above to make predictions on the test data set?

Answer 1

正如@DmitryPolonskiy评论的那样，模型必须经过训练（使用fit方法）才能用于predict。

# Train the model (a.k.a. `fit` training data to it).
lr.fit(train_df[features_columns], train_df["target"])
# Use the model to make predictions based on testing data.
y_pred = lr.predict(test_df[feature_columns])
# Compare the predicted y values to actual y values.
accuracy = (y_pred == test_df["target"]).mean()

cross_val_predict是一种交叉验证方法，可让您确定模型的准确性。看看sklearn's cross-validation page。

Answer 2

在预测之前，我认为cross_val_score或cross_val_predict不适合。它在飞行中完成。如果你看一下documentation (section 3.1.1.1)，你会发现他们从来没有在任何地方提到合适。

Using cross_val_predict against test data set

2 个答案: