Practical Data Science using Python - Decision Tree - Iris Dataset Case Study

The video tutorial explains decision trees using the iris dataset, covering data preparation, exploratory data analysis, model building, and evaluation. It highlights the issue of overfitting and demonstrates how to visualize the decision tree using Graphviz. The tutorial provides a comprehensive understanding of decision trees, including the importance of train-test split and the interpretation of confusion matrices.

What is the primary goal when using decision trees with the Iris dataset?

• To classify flowers into different species

• To determine the color of the flowers

• To predict the length of petals

• To calculate the average width of sepals

Which libraries are essential for loading and analyzing the Iris dataset?

• OpenCV and PIL

• NLTK and SpaCy

• Pandas and NumPy

• TensorFlow and Keras

What is the purpose of splitting the dataset into training and testing sets?

• To reduce the number of features

• To improve the speed of the model

• To ensure the model is tested on unseen data

• To increase the size of the dataset

What is a common issue with decision trees if they are left unconstrained?

• They become too simple

• They ignore important features

• They require more data

• They overfit the training data

How can overfitting in decision trees be identified?

• By checking if the accuracy is below 50%

• By observing a perfect accuracy score on training data

• By ensuring the model runs faster

• By reducing the number of features

What does a Gini score of 0 in a decision tree node indicate?

• The node is impure

• The node is perfectly pure

• The node has missing values

• The node needs further splitting

What is the role of the Graphviz library in decision tree analysis?

• To clean the dataset

• To perform statistical analysis

• To optimize the decision tree

• To visualize the decision tree structure

Which feature is used as the root node in the decision tree for the Iris dataset?

• Sepal length

• Petal width

• Sepal width

• Petal length

What is the significance of a leaf node in a decision tree?

• It is used for data preprocessing

• It shows a split in the data

• It represents a class label

• It indicates a decision point

Why is it important to visualize a decision tree?

• To increase the dataset size

• To understand the decision-making process

• To reduce the number of features

• To improve the model's speed