i'd like to create few Random forest model within a for loop in which i move the number of estimators. Train each of them on the same data sample and measure the accuracy of each. This is my beginning code:
r = range(0, 100)
for i in r:
RF_model_%i = RandomForestClassifier(criterion="entropy", n_estimators=i, oob_score=True)
RF_model_%i.fit(X_train, y_train)
y_predict = RF_model_%i.predict(X_test)
accuracy_%i = accuracy_score(y_test, y_predict)
what i like to understand is:
You can do something like this:
results = [] # init
r = range(0, 100)
for i in r:
RF_model_%i = RandomForestClassifier(criterion="entropy", n_estimators=i, oob_score=True)
RF_model_%i.id = i # dynamically add fields to objects
RF_model_%i.fit(X_train, y_train)
y_predict = RF_model_%i.predict(X_test)
accuracy_%i = accuracy_score(y_test, y_predict)
results.append(accuracy_%i) # put the result on a list within the for-loop
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