Discuss the importance of data : Classification tree in Python: Training

What is the main difference between a regression tree and a classification tree?

• Regression trees predict continuous values, while classification trees predict categorical values.

• Classification trees use Decision Tree Regressor.

• Classification trees predict continuous values, while regression trees predict categorical values.

• Regression trees use Decision Tree Classifier.

Why is it important to set a maximum depth for a decision tree?

• To avoid overfitting the training data.

• To prevent the tree from underfitting the data.

• To ensure the tree is as large as possible.

• To make the tree more complex.

What happens if you do not set a maximum depth for a decision tree?

• The tree will not be able to make predictions.

• The tree will underfit the training data.

• The tree will likely overfit the training data.

• The tree will be too small.

What is the default criterion used by the Decision Tree Classifier?

• Cross entropy

• Classification error rate

• Gini

• Mean squared error

What method is used to predict values using a classifier object?

• dot calculate

• dot predict

• dot transform

• dot fit

What does an accuracy score of 60% indicate?

• The model has a 60% chance of overfitting.

• 60% of the data is used for training.

• The model correctly identifies 60% of the Y values.

• 60% of the model's predictions are incorrect.

In a confusion matrix, what does a 'true negative' represent?

• An actual positive correctly predicted as positive.

• An actual negative correctly predicted as negative.

• An actual negative predicted as positive.

• An actual positive predicted as negative.

What does the color blue represent in a decision tree plot?

• High purity of zeros

• High purity of ones

• No purity

• Equal distribution of zeros and ones

What is the purpose of using 'minimum sample size' in a decision tree?

• To make the tree more complex.

• To decrease the tree's depth.

• To ensure each leaf has a minimum number of samples.

• To increase the number of branches.

How can you improve the accuracy of a decision tree model?

• By reducing the number of features.

• By tuning hyperparameters like maximum depth and minimum sample size.

• By increasing the maximum depth indefinitely.

• By using a different dataset.