AWS Certified Data Analytics Specialty 2021 – Hands-On - Classification Models

The video tutorial covers the basics of machine learning model types, focusing on regression and classification, including binary and multiclass classification. It explains how Amazon's machine learning service uses linear and logistic regression models. The tutorial also introduces the concept of confusion matrices for evaluating model accuracy and discusses the importance of hyperparameters and their tuning in optimizing machine learning models.

What is the primary goal of a regression model in machine learning?

• To classify data into categories

• To predict a numerical value

• To cluster similar data points

• To identify patterns in text

Which type of classification problem involves predicting one of two possible outcomes?

• Binary classification

• Clustering

• Regression

• Multiclass classification

What type of regression is used for predicting categories in Amazon's machine learning service?

• Polynomial regression

• Logistic regression

• Ridge regression

• Linear regression

What does a confusion matrix help visualize in a multiclass classification model?

• The complexity of the model

• The accuracy of predictions

• The speed of the algorithm

• The size of the dataset

In a confusion matrix, what does a dark blue color along the diagonal indicate?

• Random predictions

• Low accuracy

• High error rate

• Perfect classification

What is the purpose of hyperparameter tuning in machine learning?

• To increase the dataset size

• To simplify the model

• To improve model accuracy

• To reduce computation time

Which of the following is an example of a hyperparameter in Amazon's machine learning service?

• Data type

• Learning rate

• Algorithm version

• Output format