Created
March 8, 2016 18:47
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from sklearn.datasets import load_digits | |
from sklearn.ensemble import RandomForestClassifier | |
from sklearn.cross_validation import train_test_split | |
from sklearn.metrics import recall_score | |
import numpy as np | |
import pandas as pd | |
digits = load_digits(10) | |
features, labels = digits['data'], digits['target'] | |
X_train, X_test, y_train, y_test = train_test_split(features, labels, train_size=0.75, test_size=0.25) | |
clf = RandomForestClassifier(n_estimators=100, n_jobs=-1) | |
clf.fit(X_train, y_train) | |
def balanced_accuracy(result): | |
all_classes = list(set(result['class'].values)) | |
all_class_accuracies = [] | |
for this_class in all_classes: | |
this_class_accuracy = len(result[(result['guess'] == this_class) & (result['class'] == this_class)])\ | |
/ float(len(result[result['class'] == this_class])) | |
all_class_accuracies.append(this_class_accuracy) | |
balanced_accuracy = np.mean(all_class_accuracies) | |
return balanced_accuracy | |
predictions = clf.predict(X_test) | |
print('Macro-averaged recall:\t', recall_score(y_test, predictions, average='macro')) | |
data = pd.DataFrame({'class': y_test, | |
'guess': predictions}) | |
print('Balanced accuracy:\t', balanced_accuracy(data)) |
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