Scikit-Learn DecisionTreeClassifier Model

Decision Tree is a versatile classification algorithm that can handle both binary and multi-class classification problems. It constructs a tree-like model of decisions based on the input features.

The key hyperparameters of DecisionTreeClassifier include the criterion (which measures the quality of a split), max_depth (the maximum depth of the tree), and min_samples_split (the minimum number of samples required to split an internal node).

The algorithm is appropriate for binary and multi-class classification problems.

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score

# generate a synthetic dataset
X, y = make_classification(n_samples=100, n_features=5, n_classes=2, random_state=1)

# split into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)

# create the DecisionTreeClassifier model
model = DecisionTreeClassifier()

# fit the model on the training data
model.fit(X_train, y_train)

# evaluate the model on the test data
yhat = model.predict(X_test)
acc = accuracy_score(y_test, yhat)
print('Accuracy: %.3f' % acc)

# make a prediction on a new sample
row = [[-1.10325445, -0.49821356, -0.05962247, -0.89224592, -0.70158632]]
yhat = model.predict(row)
print('Predicted: %d' % yhat[0])

Running the example gives an output like:

Accuracy: 0.900
Predicted: 0

The steps are as follows:

1. Generate synthetic dataset:

   • Create a dataset using make_classification(), with specified samples and classes.
   • Split the dataset into training and testing sets using train_test_split().

2. Create and fit model:

   • Instantiate DecisionTreeClassifier with default hyperparameters.
   • Fit the model on the training data using fit().

3. Evaluate model:

   • Predict on the test data using predict().
   • Calculate accuracy with accuracy_score().

4. Make a single prediction:

   • Pass a new sample to predict() and print the result.

This example demonstrates the straightforward application of DecisionTreeClassifier for classification tasks, highlighting its ease of use and interpretability in scikit-learn.