i'm analyzing training error , validation error of decision tree model using tree package of sklearn. #compute rms error def compute_error(x, y, model): yfit = model.predict(x.toarray()) return np.mean(y != yfit) def drawlearningcurve(model,xtrain, ytrain, xtest, ytest): sizes = np.linspace(2, 25000, 50).astype(int) train_error = np.zeros(sizes.shape) crossval_error = np.zeros(sizes.shape) i,size in enumerate(sizes): model = model.fit(xtrain[:size,:].toarray(),ytrain[:size]) #compute validation error crossval_error[i] = compute_error(xtest,ytest,model) #compute training error train_error[i] = compute_error(xtrain[:size,:],ytrain[:size],model) sklearn import tree clf = tree.decisiontreeclassifier() drawlearningcurve(clf, xtr, ytr, xte, yte) the problem (i don't know whether problem) if give decision tree model function drawlearningcurve , receive result of training error 0.0 in each loop. related nature of dataset, or of tree package of sklearn? ...