What are the simplest examples of Machine Learning algorithms?

Question

Could someone tell me some of the simplest examples of Machine Learning algorithms?

Answer 1

Following are the main algorithms used in Machine Learning:

1. Linear Regression

2. Decision Trees

3. Support Vector Machine

• Linear Regression: Linear Regression is probably one of the most prominent and easy algorithms in statistics and machine learning. Regression is basically a method of modeling a target value based on independent predictors. It is generally used for forecasting and finding out cause-and-effect relationships between variables. Regression techniques generally differ on the basis of a number of independent variables and the type of relationship between the independent and dependent variables.

Linear regression is that type of regression analysis where the number of independent variables is one and there is a linear relationship between the independent(x) and dependent(y) variables.

Equation is given as : y = a_0 + a_1 * x

• Decision Trees: It is a tree-shaped algorithm that is used to determine a course of action. Every tree branch represents a possible decision, occurrence, or reaction. Decision Tree Analysis is a general, predictive modeling tool that has applications spanning a number of different areas. Mostly, decision trees are constructed via an algorithmic approach that identifies ways to split a data set based on different conditions. It is the most usable and practical method for supervised learning. Decision Trees are a non-parametric supervised learning method used for both classification and regression tasks. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.

If the tree is deeper then there will be more complexity of rules and the model will be fitter.

• Support Vector Machine:  It is actually a supervised learning method that analyzes data and recognizes patterns. The standard SVM takes a set of input data and predicts, for each given input, which of two possible classes comprises the input.

SVM model is used under this approach to find the relationship between object-oriented matrices and fault proneness empirically evaluated using the KC1 NASA data set of a storage management system for ground data written in C++ with 145 classes and 2107 methods and 40 KLOC.

Here is the list of properties of SVM :

1. Duality

2. Kernels

3. Margin

4. Convexity

5. Sparseness

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See this Machine Learning Course for more information :

Answer 2

Following are the main algorithms used in Machine Learning:

1. Linear Regression

2. Decision Trees

3. Support Vector Machine

• Linear Regression: Linear Regression is probably one of the most prominent and easy algorithms in statistics and machine learning. Regression is basically a method of modeling a target value based on independent predictors. It is generally used for forecasting and finding out cause-and-effect relationships between variables. Regression techniques generally differ on the basis of a number of independent variables and the type of relationship between the independent and dependent variables.

Linear regression is that type of regression analysis where the number of independent variables is one and there is a linear relationship between the independent(x) and dependent(y) variables.

Equation is given as : y = a_0 + a_1 * x

• Decision Trees: It is a tree-shaped algorithm that is used to determine a course of action. Every tree branch represents a possible decision, occurrence, or reaction. Decision Tree Analysis is a general, predictive modeling tool that has applications spanning a number of different areas. Mostly, decision trees are constructed via an algorithmic approach that identifies ways to split a data set based on different conditions. It is the most usable and practical method for supervised learning. Decision Trees are a non-parametric supervised learning method used for both classification and regression tasks. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.

If the tree is deeper then there will be more complexity of rules and the model will be fitter.

• Support Vector Machine:  It is actually a supervised learning method that analyzes data and recognizes patterns. The standard SVM takes a set of input data and predicts, for each given input, which of two possible classes comprises the input.

SVM model is used under this approach to find the relationship between object-oriented matrices and fault proneness empirically evaluated using the KC1 NASA data set of a storage management system for ground data written in C++ with 145 classes and 2107 methods and 40 KLOC.

Here is the list of properties of SVM :

1. Duality

2. Kernels

3. Margin

4. Convexity

5. Sparseness

If you want to become an expert in Machine Learning, check out this Machine Learning course offered by Intellipaat.