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What is a decision tree?

A decision tree is a tree-like model that classifies data by splitting it with questions.

What is a decision tree?

A decision tree splits the data step by step with yes/no questions: "height > 170?", "age < 30?". Each inner node is a question and each leaf a decision.

from sklearn.tree import DecisionTreeClassifier, export_text
X = [[20, 0], [25, 1], [40, 0], [50, 1]]   # age, home owner?
y = [0, 0, 1, 1]
m = DecisionTreeClassifier(random_state=0).fit(X, y)
print(export_text(m, feature_names=["yas", "ev"]))

Trees are interpretable: why the model made a decision can be explained by the rules read up to the root.

No scaling needed

Trees compare each feature with a threshold on its own; they don't compute distances. So scaling doesn't change the result, and they work directly with mixed-unit data.

print(m.get_depth())      # depth of the tree
print(m.get_n_leaves())   # number of leaves

The same family works for regression: DecisionTreeRegressor predicts the mean of the examples in that region at the leaves.

Practice Build a tree

Train a DecisionTreeClassifier(random_state=0); print the predictions for [[30, 1]] and [[45, 0]], then the depth of the tree.

Starter code

model.py
from sklearn.tree import DecisionTreeClassifier

X = [[20, 0], [25, 1], [40, 0], [50, 1]]
y = [0, 0, 1, 1]

# buraya yaz

Sample solution

model.py
from sklearn.tree import DecisionTreeClassifier

X = [[20, 0], [25, 1], [40, 0], [50, 1]]
y = [0, 0, 1, 1]

m = DecisionTreeClassifier(random_state=0).fit(X, y)
print(m.predict([[30, 1], [45, 0]]))
print(m.get_depth())

Expected output

[0 1]
1

Questions and answers

Review the lesson questions together with their answers. To test yourself, solve the lesson interactively.

1 What does a leaf node represent in a decision tree?

  • A feature
  • The training error
  • The final decision (prediction) Correct answer
  • A question

Explanation: Inner nodes ask questions; leaves give a class or value.

2 Print the tree's rules as text.

Question 
print(____(m, feature_names=["yas", "ev"]))
Answer 
print(export_text(m, feature_names=["yas", "ev"]))

Explanation: export_text gives the tree as indented rules.

3 Match the parts.

Term Meaning
root The first question
internal node An intermediate question
leaf Decision
get_depth() Length of the question chain

All lessons in Beginner

1 What is ML?

  1. What is machine learning?
  2. Supervised and unsupervised
  3. Data and model
  4. Training and testing
  5. Feature and label notation
  6. Summary - ML fundamentals

2 NumPy basics

  1. What is NumPy?
  2. Creating arrays
  3. Array properties
  4. Array indexing
  5. Reshaping arrays
  6. Summary - NumPy basics

3 Pandas basics

  1. What is Pandas?
  2. Creating a DataFrame
  3. Column operations
  4. Selecting and filtering data
  5. Viewing first rows
  6. Summary - Pandas basics

4 Data preprocessing

  1. Data cleaning
  2. Missing data
  3. Data types
  4. Label encoding
  5. Data splitting
  6. Summary - Data preprocessing

5 Statistics basics

  1. Mean
  2. Median
  3. Standard deviation
  4. Variance
  5. Correlation
  6. Summary - Statistics

6 Linear algebra

  1. Vectors
  2. Matrices
  3. Matrix multiplication
  4. Transpose
  5. Identity matrix
  6. Summary - Linear algebra

7 Project: Data analysis

  1. Loading data
  2. Cleaning data
  3. Computing statistics
  4. Printing results

8 Regression

  1. What is regression?
  2. Linear regression
  3. Model training
  4. Prediction
  5. Error measurement
  6. Summary - Regression

9 KNN

  1. What is KNN?
  2. The K parameter
  3. Distance measurement
  4. Classification
  5. Model training
  6. Summary - KNN

10 Decision trees

  1. What is a decision tree?
  2. Splitting criterion
  3. Depth
  4. Classification
  5. Overfitting
  6. Summary - Decision trees

11 Classification

  1. What is classification?
  2. Binary classification
  3. Multi-class
  4. Accuracy
  5. Confusion matrix
  6. Summary - Classification

12 Model evaluation

  1. Train-test split
  2. Accuracy
  3. Precision
  4. Recall
  5. F1 score
  6. Summary - Model evaluation

13 Project: Classification

  1. Preparing data
  2. Training model
  3. Prediction
  4. Evaluation

14 Scaling

  1. What is scaling?
  2. StandardScaler
  3. MinMaxScaler
  4. When to use?
  5. Pipeline
  6. Summary - Scaling

15 Feature engineering

  1. What is a feature?
  2. Feature selection
  3. Feature extraction
  4. Categorical features
  5. Numerical features
  6. Summary - Feature engineering

16 Model selection

  1. What is model selection?
  2. Cross-validation
  3. Grid search
  4. Score comparison
  5. Hyperparameter
  6. Summary - Model selection

17 Project: Regression

  1. Preparing data
  2. Training model
  3. Prediction
  4. Error measurement

18 Metrics

  1. What is a metric?
  2. Accuracy
  3. Precision
  4. Recall
  5. F1 score
  6. Summary - Metrics

19 Data visualization

  1. What is visualization?
  2. Scatter plot
  3. Histogram
  4. Line plot
  5. Box plot
  6. Summary - Visualization

20 Beginner final

  1. Challenge 1
  2. Challenge 2
  3. Challenge 3
  4. Challenge 4