playing with randomtreeclassifier

Ensemble_learning/mnist_ensemble.py

@@ -2,6 +2,7 @@
 ''' retrieving mnist data '''
 
 from sklearn.datasets import fetch_mldata
+import numpy as np
 mnist = fetch_mldata('MNIST original')
 print(mnist)
 
@@ -14,6 +15,25 @@
 
 #%%
 X_train,Y_train,X_val,Y_val,X_test,Y_test = X[:40000],Y[:40000],X[40000:50000],Y[40000:50000],X[50000:],Y[50000:]
+shuffle_index = np.random.permutation(40000)
+X_train, Y_train = X_train[shuffle_index], Y_train[shuffle_index]
+X_train_array = np.array(X_train)
+Y_train_array = np.array(Y_train)
+X_val_array = np.array(X_val)
+Y_val_array = np.array(Y_val)
+X_test_array = np.array(X_test)
+Y_test_array = np.array(Y_test)
+
 print(X_val)
 
 #%%
+''' training of RandomForestClassifier'''
+from sklearn.metrics import accuracy_score
+from sklearn.ensemble import RandomForestClassifier
+
+rnd_clf = RandomForestClassifier(n_estimators=1000,n_jobs=100,
+random_state=42)
+rnd_clf.fit(X_train_array,Y_train_array)
+rnd_pred = rnd_clf.predict(X_val_array)
+rnd_eff = accuracy_score(Y_val_array,rnd_pred)
+print(rnd_eff)