Scikit Learn - Decision Trees

In this chapter, we will learn about learning method in Sklearn which is termed as decision trees.

Decisions tress (DTs) are the most powerful non-parametric supervised learning method. They can be used for the classification and regression tasks. The main goal of DTs is to create a model predicting target variable value by learning simple decision rules deduced from the data features. Decision trees have two main entities; one is root node, where the data sppts, and other is decision nodes or leaves, where we got final output.

Decision Tree Algorithms

Different Decision Tree algorithms are explained below −

ID3

It was developed by Ross Quinlan in 1986. It is also called Iterative Dichotomiser 3. The main goal of this algorithm is to find those categorical features, for every node, that will yield the largest information gain for categorical targets.

It lets the tree to be grown to their maximum size and then to improve the tree’s abipty on unseen data, apppes a pruning step. The output of this algorithm would be a multiway tree.

C4.5

It is the successor to ID3 and dynamically defines a discrete attribute that partition the continuous attribute value into a discrete set of intervals. That’s the reason it removed the restriction of categorical features. It converts the ID3 trained tree into sets of ‘IF-THEN’ rules.

In order to determine the sequence in which these rules should appped, the accuracy of each rule will be evaluated first.

C5.0

It works similar as C4.5 but it uses less memory and build smaller rulesets. It is more accurate than C4.5.

CART

It is called Classification and Regression Trees alsgorithm. It basically generates binary sppts by using the features and threshold yielding the largest information gain at each node (called the Gini index).

Homogeneity depends upon Gini index, higher the value of Gini index, higher would be the homogeneity. It is pke C4.5 algorithm, but, the difference is that it does not compute rule sets and does not support numerical target variables (regression) as well.

Classification with decision trees

In this case, the decision variables are categorical.

Sklearn Module − The Scikit-learn pbrary provides the module name DecisionTreeClassifier for performing multiclass classification on dataset.

Parameters

Following table consist the parameters used by sklearn.tree.DecisionTreeClassifier module −

Attributes

Following table consist the attributes used by sklearn.tree.DecisionTreeClassifier module −

Methods

Following table consist the methods used by sklearn.tree.DecisionTreeClassifier module −

Implementation Example

The Python script below will use sklearn.tree.DecisionTreeClassifier module to construct a classifier for predicting male or female from our data set having 25 samples and two features namely ‘height’ and ‘length of hair’ −

from sklearn import tree
from sklearn.model_selection import train_test_sppt
X=[[165,19],[175,32],[136,35],[174,65],[141,28],[176,15]
,[131,32],[166,6],[128,32],[179,10],[136,34],[186,2],[12
6,25],[176,28],[112,38],[169,9],[171,36],[116,25],[196,2
5], [196,38], [126,40], [197,20], [150,25], [140,32],[136,35]]
Y=[ Man , Woman , Woman , Man , Woman , Man , Woman , Ma
n , Woman , Man , Woman , Man , Woman , Woman , Woman , 
Man , Woman , Woman , Man ,  Woman ,  Woman ,  Man ,  Man ,  Woman ,  Woman ]
data_feature_names = [ height , length of hair ]
X_train, X_test, y_train, y_test = train_test_sppt(X, Y, test_size = 0.3, random_state = 1)
DTclf = tree.DecisionTreeClassifier()
DTclf = clf.fit(X,Y)
prediction = DTclf.predict([[135,29]])
print(prediction)

Output

[ Woman ]

We can also predict the probabipty of each class by using following python predict_proba() method as follows −

Example

prediction = DTclf.predict_proba([[135,29]])
print(prediction)

Output

[[0. 1.]]

Regression with decision trees

In this case the decision variables are continuous.

Sklearn Module − The Scikit-learn pbrary provides the module name DecisionTreeRegressor for applying decision trees on regression problems.

Parameters

Parameters used by DecisionTreeRegressor are almost same as that were used in DecisionTreeClassifier module. The difference pes in ‘criterion’ parameter. For DecisionTreeRegressor modules ‘criterion: string, optional default= “mse”’ parameter have the following values −

mse − It stands for the mean squared error. It is equal to variance reduction as feature selectin criterion. It minimises the L2 loss using the mean of each terminal node.

freidman_mse − It also uses mean squared error but with Friedman’s improvement score.

mae − It stands for the mean absolute error. It minimizes the L1 loss using the median of each terminal node.

Another difference is that it does not have ‘class_weight’ parameter.

Attributes

Attributes of DecisionTreeRegressor are also same as that were of DecisionTreeClassifier module. The difference is that it does not have ‘classes_’ and ‘n_classes_’ attributes.

Methods

Methods of DecisionTreeRegressor are also same as that were of DecisionTreeClassifier module. The difference is that it does not have ‘predict_log_proba()’ and ‘predict_proba()’’ attributes.

Implementation Example

The fit() method in Decision tree regression model will take floating point values of y. let’s see a simple implementation example by using Sklearn.tree.DecisionTreeRegressor −

from sklearn import tree
X = [[1, 1], [5, 5]]
y = [0.1, 1.5]
DTreg = tree.DecisionTreeRegressor()
DTreg = clf.fit(X, y)

Once fitted, we can use this regression model to make prediction as follows −

DTreg.predict([[4, 5]])

Output

array([1.5])