AI Chap. 2

AI Chap. 2

1. Label encoding e process of transforming the word labels into numerical form. This enables the algorithms to operate on our data.

2. Logistic Regression classifier. Used to explain the relationship between input variables and output variables.

3. Naïve Bayes classifier. Used to build classifiers using Bayes theorem. Describes the probability of an event occurring based on different conditions that are related to this event.

4. Confusion matrix. A figure or a table that is used to describe the performance of a classifier. It is usually extracted from a test dataset for which the ground truth is known.

5. Support Vector Machines. A classifier that is defined using a separating hyperplane between the classes.Finds the optimal hyperplane that separates the training data into two classes.

6. Classifying income data using Support Vector Machines. Build a Support Vector Machine classifier to predict the income bracket of a given person based on 14 attributes.

7. What is Regression? Regression is the process of estimating the relationship between input and output variables. One thing to note is that the output variables are continuous-valued real numbers. Hence there are an infinite number of possibilities. This is in contrast with classification, where the number of output classes is fixed.

8. Supervised learning refers to the process of building a machine learning model that is based on labeled training data.

9. Unsupervised learning refers to the process of building a machine learning model without relying on labeled training data.