# Machine Learning

## Dimensionality reduction in Machine Learning

The complexity of any classification or regression algorithm depends on the number of inputs to the model. This determines the time and space complexity and the necessary number of training examples to train such a classification or regression algorithm. In this article, we discuss what is dimensionality reduction, how dimensionality reduction is implemented, and the …

## Entropy and Information Gain in Decision Tree Learning

Explain the concepts of Entropy and Information Gain in Decision Tree Learning. While constructing a decision tree, the very first question to be answered is, Which Attribute Is the Best Classifier? The central choice in the ID3 algorithm is selecting which attribute to test at each node in the tree. We would like to select …

## Appropriate Problems For Decision Tree Learning

What are appropriate problems for Decision tree learning? Although a variety of decision tree learning methods have been developed with somewhat differing capabilities and requirements, decision tree learning is generally best suited to problems with the following characteristics: 1. Instances are represented by attribute-value pairs. “Instances are described by a fixed set of attributes (e.g., …

## Decision Tree Representation in Machine Learning

What are decision tree and decision tree learning? Explain the representation of the decision tree with an example. Decision Trees is one of the most widely used Classification Algorithm Features of Decision Tree Learning Method for approximating discrete-valued functions (including boolean) Learned functions are represented as decision trees (or if-then-else rules) Expressive hypotheses space, including …

## Perspectives and Issues in Machine Learning

Perspectives and Issues in Machine Learning Following are the list of issues in machine learning: 1. What algorithms exist for learning general target functions from specific training examples? In what settings will particular algorithms converge to the desired function, given sufficient training data? Which algorithms perform best for which types of problems and representations? 2. …

## List then Eliminate Algorithm Machine Learning

Consistent Hypothesis, Version Space and List then Eliminate Algorithm Consistent Hypothesis The idea: output a description of the set of all hypotheses consistent with the training examples (correctly classify training examples). Version Space: a representation of the set of hypotheses that are consistent with D an explicit list of hypotheses (List-Then-Eliminate) a compact representation of …

## Means-Ends Analysis Artificial Intelligence

Means-Ends Analysis Search Technique (Algorithm) – Artificial Intelligence In this tutorial, I will how to apply Means-Ends Analysis Search Technique (Algorithm) to solve the given problem. Video Tutorial – Means-Ends Analysis Means-Ends Analysis In Artificial Intelligence, we have studied many search strategies which traverse either in forward or backward direction, but a mixture of these …

## Simple Linear Regression Model Solved Example

Simple Linear Regression Model Solved Example in Machine Learning Regression modeling is a process of determining a relationship between one or more independent variables and one dependent or output variable. Example: 1. Predicting the height of a person given the age of the person. 2. Predicting the price of the car given the car model, …

## Quadratic Polynomial Regression Model Solved Example

Quadratic Polynomial Regression Model Solved Example in Machine Learning Regression modeling is a process of determining a relationship between one or more independent variables and one dependent or output variable. Example: 1. Predicting the price of the car given the car model, year of manufacturing, mileage, engine capacity. 2. Predicting the height of a person …

## Types of Regression Models

Types of Regression Models in Machine Learning Regression modeling is a process of determining a relationship between one or more independent variables and one dependent or output variable. Examples: 1. Predicting the price of the car given the car model, year of manufacturing, mileage, engine capacity, etc. 2. Predicting the height of a person given …