cross validation + decision trees in sklearn

Question

Attempting to create a decision tree with cross validation using sklearn and panads.

My question is in the code below, the cross validation splits the data, which i then use for both training and testing. I will be attempting to find the best depth of the tree by recreating it n times with different max depths set. In using cross validation should i instead be using k folds CV and if so how would I use that within the code I have?

import numpy as np import pandas as pd from sklearn import tree from sklearn import cross_validation  features = ["fLength", "fWidth", "fSize", "fConc", "fConc1", "fAsym", "fM3Long", "fM3Trans", "fAlpha", "fDist", "class"]  df = pd.read_csv('magic04.data',header=None,names=features)  df['class'] = df['class'].map({'g':0,'h':1})  x = df[features[:-1]] y = df['class']  x_train,x_test,y_train,y_test = cross_validation.train_test_split(x,y,test_size=0.4,random_state=0)  depth = [] for i in range(3,20):     clf = tree.DecisionTreeClassifier(max_depth=i)     clf = clf.fit(x_train,y_train)     depth.append((i,clf.score(x_test,y_test))) print depth 

here is a link to the data that i am using in case that helps anyone. https://archive.ics.uci.edu/ml/datasets/MAGIC+Gamma+Telescope

Answer 1

In your code you are creating a static training-test split. If you want to select the best depth by cross-validation you can use sklearn.cross_validation.cross_val_score inside the for loop.

You can read sklearn's documentation for more information.

Here is an update of your code with CV:

import numpy as np import pandas as pd from sklearn import tree from sklearn.cross_validation import cross_val_score from pprint import pprint  features = ["fLength", "fWidth", "fSize", "fConc", "fConc1", "fAsym", "fM3Long", "fM3Trans", "fAlpha", "fDist", "class"]  df = pd.read_csv('magic04.data',header=None,names=features) df['class'] = df['class'].map({'g':0,'h':1})  x = df[features[:-1]] y = df['class']  # x_train,x_test,y_train,y_test = cross_validation.train_test_split(x,y,test_size=0.4,random_state=0) depth = [] for i in range(3,20):     clf = tree.DecisionTreeClassifier(max_depth=i)     # Perform 7-fold cross validation      scores = cross_val_score(estimator=clf, X=x, y=y, cv=7, n_jobs=4)     depth.append((i,scores.mean())) print(depth) 

Alternatively, you can use sklearn.grid_search.GridSearchCV and not write the for loop yourself, especially if you want to optimize for more than one hyper-parameter.

import numpy as np import pandas as pd from sklearn import tree from sklearn.model_selection import GridSearchCV  features = ["fLength", "fWidth", "fSize", "fConc", "fConc1", "fAsym", "fM3Long", "fM3Trans", "fAlpha", "fDist", "class"]  df = pd.read_csv('magic04.data',header=None,names=features) df['class'] = df['class'].map({'g':0,'h':1})  x = df[features[:-1]] y = df['class']   parameters = {'max_depth':range(3,20)} clf = GridSearchCV(tree.DecisionTreeClassifier(), parameters, n_jobs=4) clf.fit(X=x, y=y) tree_model = clf.best_estimator_ print (clf.best_score_, clf.best_params_)  

Edit: changed how GridSearchCV is imported to accommodate learn2day's comment.