Decision tree classifier in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the DecisionTreeClassifier from scikit-learn.

ML Python

from sklearn.tree import [1]

Drag options to blanks, or click blank then click option'

ADecisionTreeClassifier

BKNeighborsClassifier

CLinearRegression

DRandomForestClassifier

Attempts:

3 left

💡 Hint

Common Mistakes

Importing a different model like RandomForestClassifier or LinearRegression.

Misspelling the class name.

The DecisionTreeClassifier is the class used to create decision tree models for classification tasks in scikit-learn.

2fill in blank

medium

Complete the code to create a decision tree classifier object with a maximum depth of 3.

ML Python

model = DecisionTreeClassifier([1]=3)

Drag options to blanks, or click blank then click option'

Amin_samples_split

Brandom_state

Cmax_depth

Dcriterion

Attempts:

3 left

💡 Hint

Common Mistakes

Using min_samples_split instead of max_depth.

Confusing random_state with depth.

The max_depth parameter limits the depth of the tree to prevent overfitting.

3fill in blank

hard

Fix the error in the code to train the decision tree model on features X and labels y.

ML Python

model.fit([1], y)

Drag options to blanks, or click blank then click option'

AX

Bfit

Cy

Dmodel

Attempts:

3 left

💡 Hint

Common Mistakes

Passing labels y as the first argument.

Passing the model itself as an argument.

The fit method requires the feature data as the first argument and the labels as the second.

4fill in blank

hard

Fill both blanks to predict labels for test data and store the predictions in variable 'predictions'.

ML Python

predictions = model.[1]([2])

Drag options to blanks, or click blank then click option'

Apredict

BX_test

Cfit

Dy_test

Attempts:

3 left

💡 Hint

Common Mistakes

Using fit instead of predict.

Passing labels y_test instead of features.

The predict method is used to get predictions from the model using test features X_test.

5fill in blank

hard

Fill all three blanks to calculate and print the accuracy score of the model predictions.

ML Python

from sklearn.metrics import {{BLANK_1 }}
accuracy = [2](y_test, {{BLANK_3}})
print(f"Accuracy: {accuracy:.2f}")

Drag options to blanks, or click blank then click option'

Aaccuracy_score

Cpredictions

Dy_train

Attempts:

3 left

💡 Hint

Common Mistakes

Importing the wrong metric function.

Using training labels y_train instead of test labels.

Passing wrong variables to the accuracy function.

The accuracy_score function compares true labels y_test with predicted labels predictions to compute accuracy.

Practice

1. What is the main purpose of a Decision Tree Classifier in machine learning?

easy

A. To store data in a database

B. To perform mathematical calculations on numbers

C. To generate random numbers for simulations

D. To split data into groups based on simple questions for classification

Solution

1. Step 1: Understand the role of decision trees

   Decision tree classifiers split data by asking simple yes/no questions to separate data into classes.

2. Step 2: Compare options with the purpose

   Only To split data into groups based on simple questions for classification describes splitting data for classification, which is the main use of decision trees.

3. Final Answer:

   To split data into groups based on simple questions for classification -> Option D

4. Quick Check:

   Decision tree purpose = classification by splitting [OK]

Hint: Decision trees split data step-by-step for classification [OK]

Common Mistakes:

• Confusing decision trees with random number generators
• Thinking decision trees store data like databases
• Assuming decision trees perform complex math calculations

2. Which of the following is the correct way to create a Decision Tree Classifier in Python using scikit-learn?

easy

A. from sklearn.tree import DecisionTreeClassifier
model = DecisionTreeClassifier()

B. from sklearn.tree import DecisionTree
model = DecisionTree()

C. import DecisionTreeClassifier from sklearn
model = DecisionTreeClassifier()

D. from sklearn.classifier import DecisionTreeClassifier
model = DecisionTreeClassifier()

Solution

1. Step 1: Recall correct import syntax

   The correct import for decision tree classifier is from sklearn.tree import DecisionTreeClassifier.

2. Step 2: Check object creation

   Creating the model with DecisionTreeClassifier() is correct. Other options have wrong imports or class names.

3. Final Answer:

   from sklearn.tree import DecisionTreeClassifier model = DecisionTreeClassifier() -> Option A

4. Quick Check:

   Correct import and class name = from sklearn.tree import DecisionTreeClassifier model = DecisionTreeClassifier() [OK]

Hint: Use sklearn.tree and DecisionTreeClassifier() to create model [OK]

Common Mistakes:

• Using wrong import paths like sklearn.classifier
• Misspelling class name as DecisionTree
• Incorrect import syntax like import DecisionTreeClassifier from sklearn

3. Given the following code, what will be the predicted class for the input X_test = [[5, 3]]?

from sklearn.tree import DecisionTreeClassifier
X_train = [[2, 3], [10, 15], [6, 8], [1, 1]]
y_train = [0, 1, 1, 0]
model = DecisionTreeClassifier(max_depth=2)
model.fit(X_train, y_train)
print(model.predict(X_test))

medium

A. [0]

B. [1]

C. [2]

D. Error: max_depth must be greater than 2

Solution

1. Step 1: Understand training data and labels

   Training data has points labeled 0 or 1. Points near [5,3] are closer to class 1 points like [6,8].

2. Step 2: Predict with trained model

   The decision tree with max_depth=2 will classify [5,3] as class 1 based on training data proximity.

3. Final Answer:

   [1] -> Option B

4. Quick Check:

   Prediction for [5,3] = [1] [OK]

Hint: Check nearest training points to predict class [OK]

Common Mistakes:

• Assuming max_depth=2 causes error
• Predicting class 2 which does not exist
• Ignoring training labels when predicting

4. What is wrong with this code snippet for training a Decision Tree Classifier?

from sklearn.tree import DecisionTreeClassifier
model = DecisionTreeClassifier(max_depth=-1)
model.fit(X_train, y_train)

medium

A. X_train and y_train are not defined

B. DecisionTreeClassifier does not have max_depth parameter

C. max_depth cannot be negative; it should be null or positive

D. fit() method requires extra parameters

Solution

1. Step 1: Check max_depth parameter validity

   max_depth must be null or a positive integer. Negative values are invalid and cause errors.

2. Step 2: Verify other code parts

   Assuming X_train and y_train are defined, and fit() requires only these two parameters, the main error is max_depth.

3. Final Answer:

   max_depth cannot be negative; it should be null or positive -> Option C

4. Quick Check:

   max_depth negative = error [OK]

Hint: max_depth must be positive or null, never negative [OK]

Common Mistakes:

• Thinking max_depth can be negative
• Assuming fit() needs more parameters
• Confusing parameter names

5. You want to avoid overfitting in a Decision Tree Classifier trained on a noisy dataset. Which approach is best to control this?

hard

A. Limit the tree depth using the max_depth parameter

B. Increase the number of features used for splitting

C. Train the tree without any depth limit

D. Use a decision tree without pruning

Solution

1. Step 1: Understand overfitting in decision trees

   Overfitting happens when the tree is too deep and fits noise instead of general patterns.

2. Step 2: Choose method to reduce overfitting

   Limiting max_depth controls tree size and prevents overfitting. Increasing features or no pruning can worsen it.

3. Final Answer:

   Limit the tree depth using the max_depth parameter -> Option A

4. Quick Check:

   Control overfitting = limit max_depth [OK]

Hint: Limit tree depth to prevent overfitting [OK]

Common Mistakes:

• Thinking more features reduce overfitting
• Believing no depth limit is better
• Ignoring pruning or depth control