edub edub - 1 month ago 7x
Python Question

How to use ExtraTreeClassifier to predict multiclass classifications

I'm quite new to machine learning techniques, and I'm having trouble following some of the scikit-learn documentation and other stackoverflow posts.. I'm trying to create a simple model from a bunch of medical data that will help me predict which of three classes a patient could fall into.

I load the data via pandas, convert all the objects to integers (Male = 0, Female=1 for example), and run the following code:

import numpy as np
import pandas as pd
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import label_binarize
from sklearn.ensemble import ExtraTreesClassifier

# Upload data file with all integers:
data = pd.read_csv('datafile.csv')

y = data["Target"]
features = list(data.columns[:-1]) # Last column being the target data
x = data[features]

ydata = label_binarize(y, classes=[0, 1, 2])
n_classes = ydata.shape[1]
X_train, X_test, y_train, y_test = train_test_split(x, ydata, test_size=.5)

model2 = ExtraTreesClassifier(), y_train)

out = model2.predict(X_test)
print np.min(out),np.max(out)

The predicted values of
range between 0.0 and 1.0, but the classes I am trying to predict are 0,1, and 2. What am I missing?


That's normal behaviour in scikit-learn.

There are two approaches possible:

A:You use "label binarize"

  • Binarizing transforms y=[n_samples, ] -> y[n_samples, n_classes] (1 dimension added; integers in range(0, X) get transformed to binary values)
  • Because of this input to fit, classifier.predict() will also return results of the form [n_predict_samples, n_classes] (with 0 and 1 as the only values) / That's what you observe!
  • Example output: [[0 0 0 1], [1 0 0 0], [0 1 0 0]] = predictions for class: 3, 0, 1

B: You skip "label binarize" (multi-class handling automatically done by sklearn)

  • Without binarizing (assuming your data is using integer-markers for classes): y=[n_samples, ]
  • Because of this input to fit, classifier.predict() will also return results of the form [n_predict_samples, ] (with possibly other values than 0, 1)
  • Example output conform to above example: [3 0 1]

Both outputs are mentioned in the docs here:


    y : array of shape = [n_samples] or [n_samples, n_outputs]
        The predicted classes.

Remark: the above behaviour should be valid for most/all classifiers! (not only ExtraTreesClassifier)