Decision Trees - classification

What does the diagram illustrate about machine learning models?

• Machine learning models can use different types of data.

• Machine learning models only use numbers.

• Machine learning models do not need data.

• Machine learning models are only for images.

What is the root in a decision tree?

• The last leaf node

• The first decision node

• The middle node

• The node with the most branches

What does it mean for a model to be "data-driven"?

• The model is created based on the training data used.

• The model never changes regardless of the data.

• The model is always perfect and error-free.

• The model is created randomly without any data.

Which of the following best describes a leaf node in a decision tree?

• The first node in the tree.

• A node that contains the most data points.

• A node that splits the data into two groups.

• A node that represents a final classification or outcome.

What type of data can be used to train a machine learning model?

• Only images.

• Only numbers.

• Only text.

• Images, text, numbers, and more.

Why is it important for a machine learning model to be trained on relevant data?

• Because the model's predictions depend on the patterns it learns from the data.

• Because the model will work without any data.

• Because the model is always perfect.

• Because the model ignores the data.

Which of the following is NOT a type of data that can be used to train a machine learning model?

• Images

• Audio files

• Text documents

• Random guesses

What is the main purpose of using a decision tree in machine learning?

• To store images only.

• To organize data for better predictions by making a series of decisions.

• To ignore the input data.

• To randomly select data points.

What is it called when a human trains a model to learn with examples?

• Unsupervised Learning

• Supervised Learning

• Training

• Labeling

Answer explanation

When a human trains a model using labeled examples, it is called Supervised Learning. This method involves providing the model with input-output pairs to learn from, enabling it to make predictions on new data.

What are different types of machine learning?

• Supervised, Unsupervised, and Reinforcement

• Automatic, Manual, and Hybrid

• Cognitive, Behavioral, and Analytical

• Static, Dynamic, and Interactive