Classification Models In Machine Learning Training Ppt

Slide 1

This slide gives an overview of logistic regression which is a sort of regression analysis approach employed when the dependent variable is discontinuous: For example, 0 or 1, true or false, and so on. The Logit function is used in Logistic Regression to assess the connection between the target variable and the independent variables.

Slide 2

This slide demonstrates that KNN is a simple algorithm that keeps all existing instances, and classifies new cases based on a majority vote of its k neighbors.

Instructor’s Notes:

KNN may be understood with an analogy from real life. For example, if you want to learn more about someone, chat with their friends and coworkers.

Consider the following before settling on the K Nearest Neighbors Algorithm:

• KNN is costly to compute & arrive at
• Variables should be normalized, or greater range variables will cause the algorithm to be biased
• Data must still be pre-processed

Slide 3

This slide states that Naive Bayes is a probabilistic Machine Learning technique based on the Bayes Theorem and is used for a wide range of classification problems. A Naive Bayesian model is straightforward to build and works well with massive datasets. It is simple to use and outperforms even the most sophisticated classification algorithms.

Slide 4

This slide showcases that the SVM algorithm is a classification process in which raw data is shown as points in an n-dimensional space (n being the number of features you have). The value of each characteristic is then assigned to a specific location, making it simple to categorize the data. Classifier lines can divide data and plot it on a graph.