I need to train a Random Forest classifier using a 3-fold cross-validation. For each sample, I need to retrieve the prediction probability when it happens to be in the test set.
I am using scikit-learn version 0.18.dev0.
This new version adds the feature to use the method cross_val_predict() with an additional parameter method to define which kind of prediction require from the estimator.
In my case I want to use the predict_proba() method, which returns the probability for each class, in a multiclass scenario.
However, when I run the method, I get as a result the matrix of prediction probabilities, where each rows represents a sample, and each column represents the prediction probability for a specific class.
The problem is that the method does not indicate which class corresponds to each column.
The value I need is the same (in my case using a RandomForestClassifier) returned in the attribute classes_ defined as:
classes_ : array of shape = [n_classes] or a list of such arrays The classes labels (single output problem), or a list of arrays of class labels (multi-output problem).
which is needed by predict_proba() because in its documentation it is written that:
The order of the classes corresponds to that in the attribute classes_.
A minimal example is the following:
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_predict
clf = RandomForestClassifier()
X = np.random.randn(10, 10)
y = y = np.array([1] * 4 + [0] * 3 + [2] * 3)
# how to get classes from here?
proba = cross_val_predict(estimator=clf, X=X, y=y, method="predict_proba")
# using the classifier without cross-validation
# it is possible to get the classes in this way:
clf.fit(X, y)
proba = clf.predict_proba(X)
classes = clf.classes_
答案 0 :(得分：2)
是的，它们将按顺序排列;这是因为DecisionTreeClassifier（这是base_estimator）uses np.unique to construct the classes_ attribute的默认RandomForestClassifier，它返回输入数组的已排序唯一值。