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**INTRODUCTION**

Machine Learning (ML), which has a variety of industrial applications, is expected to grow in the future. With a staggering CAGR (Compound Annual growth Rate) of 39%, the global Machine Learning market will reach USD 117billion by 2027. Machine Learning concepts are essential for tech-savvy people and freshers to be able to build a career in the ML field. Machine Learning has many uses, and regression **algorithms** are an important concept.

**Machine Learning** uses regression algorithms to predict future values. Regression uses historical data as input data to predict many future values. In ML, label is the target variable (to predict) and regression assists in determining the relationship between data points and label. Regression in ML is a form of supervised learning that maps a predictive relationship between data points and labels. There are many types of regression algorithms available in ML: linear, logistic, stepwise and polynomial. Continue reading to learn more about the most widely used regression algorithms.

**LIST DESTINATION ALGORITHMS FOR MACHINE LEARNING**

· **Linear Regression**

· **Ridge Regression**

· **Neural Network Regression**

· **Lasso Regression**

· **Decision tree Regression**

· **Random Forest**

· **KNN Model**

· **Support Vector Machines (SVM)**

· **Gausian Regression**

· **Polynomial Analysis**

**1** **LINEAR RESESSION**

This is the most widely used regression algorithm in Machine Learning. To predict future values (output variables), a significant variable is selected from the data set. If the labels are continuous (e.g., the number of daily flights from an airport), linear regression algorithm is used. Linear regression can be represented as y = (b*x +) c.

In the above illustration, 'y’ is the independent variable and 'x' the dependent variable. The slope of the linear regression line that gives us the output variables is called 'b' and 'c. It is also known as its intercept. Linear regression assumes that the input and output have a linear relationship. Linear regression will not allow dependent and independent variables to be plotted on the same line. This can lead to a loss of output. Linear regression calculates the loss of output as follows:

Loss function: (Predicted output and actual output) .

**2** **HTML3_ RIDGE REGRESSION**

Machine Learning also uses Ridge Regression, a popular linear regression algorithm. A linear regression ML algorithm is one that uses only one variable to predict the output. Ridge regression is preferred by ML experts because it minimizes loss in linear regression (described above). Ridge regression uses OLS (Ordinary Least Squares) to predict the output values. A ridge estimator is used in ridge regression. Linear regression, as mentioned above, uses OLS to predict output values.

Ridge regression can be used to reduce the complexity of the ML model. Ridge regression does not reduce all coefficients, but it reduces them to a greater degree than other models. The following is the ridge regression:

y = Xb + ,

where 'y" is the N*1 vector that defines the observations of the dependent data points/variables and 'X is the matrix of regressionors. 'b' refers to the N*1 vector containing regression coefficients, and 'X' refers to the vector (N*1) containing errors. IT professionals also use the ridge algorithm for data mining.

**3)** **HTML3_ HTML4_ HTML5_ HTML6_ HTML7_ HTML8_ HTML9_ HTML4_ HTML5_ HTML6_ HTML7_ NEURAL-NETWORK REGRESSION**

You all must be aware of the power of neural networks in making predictions/assumptions. A neural network's nodes have an activation function which defines the output of each node. This function is based on a set inputs. To transform a neural network into an regression model, one can manipulate the last activation function. You can use "Keras", which is the appropriate Python library to build neural networks in ML.

A neuron's output is mapped to neural network regression using a range of values, which ensures non-linearity. For neural network regression output prediction, you can either choose one parameter or several parameters. Each neuron has a weight that is associated with it. These well-connected neurons are useful in forecasting future values and mapping the relationship between dependent variables and independent variables.

**4)** **HTML3_ LASSO Regression**

Lasso (Least Absolute Shiftage and Selection Operator), regression is another popular linear ML regression (one input variable). To avoid prediction errors, the sum of coefficient values in lasso regression is penalized. Shrinkage is a technique that reduces the determination coefficients of lasso regression to zero. To make sure that they fit well with different datasets, the regression coefficients in lasso regression are decreased. The lasso algorithm can also be used to perform regression in Data Mining.

When there is high multicollinearity, ML experts prefer the lasso algorithm. Multicollinearity means that independent variables are closely related to one another. A small change in data can result in a significant change in regression coefficients. Lasso algorithm regression is a method of forecasting in ML.

**5)** **DECISION TREE**

Decision tree regression is a method for non-linear machine learning. The decision tree regression algorithm's main purpose is to divide the data into smaller sets. To plot the value of data points that are connected to the problem statement, the subsets of the dataset will be created. This algorithm creates a decision tree with decision and leaf nodes by splitting the data set. This model is preferred by ML experts in situations where there isn't enough data change.

Even a small change in data can have a significant impact on the structure of the next decision tree. You should not prune decision tree regressors excessively as it will cause a lack of end nodes to make predictions. The decision tree regressors should not be pruned excessively if there are multiple end nodes (regression output value).

**6)** **RANDOM FOREST**

Random forest is another widely-used algorithm in Machine Learning for non-linear, or non-linear, regression. A random forest, unlike decision tree regression (single trees), uses multiple decision trees to predict the output. A random data point is selected from the dataset. This algorithm then creates a decision tree with these data points. The model then predicts the value of each new data point.

Multiple decision trees can be used to predict multiple output values using a random forest algorithm. To compute the final output, you will need to find the average value of all predicted values for each new data point. A random forest algorithm has one drawback. It requires more training. This is due to the high number of decision trees that are mapped using this algorithm. It also requires more computational power.

**7)** **HTML3_ KNN MODEL**

KNN model is widely used in Machine Learning for non-linear analysis. KNN (K Nearest Neighbours), is a simple implementation method for non-linear machine learning regression. KNN assumes the new data point is comparable to the existing data points. The new data point is then compared with the existing categories and placed under a relatable categorie. This algorithm uses the average value of the k closest neighbors as an input. KNN models give each neighbor a weight that determines their contribution to the average value.

Common practice is to assign weights to neighbors using a KNN model. 1/d is where d is distance from object to be predicted. When determining the value for a new datapoint via the KNN model it is important to know that neighbors closer to the object will contribute more than those farther away.

**8)** **SUPPORT VISION MACHINES (SVM),**

SVM can be used in both linear and nonlinear regressions in ML. SVM has many uses, including image processing, segmentation, prediction of stock market patterns, and text categorization. The SVM algorithm can be used to identify outputs in multidimensional spaces. The data points in a multidimensional space are not represented in a 2D plot. In a multidimensional space, the data points can be represented as vectors.

This model creates an SVM hyperplane with a max-margin that separates classes and assigns values to each class. An SVM model cannot perform at its best when there is more noise in the data.

9. GAUSSIAN REGRESSION

Because of their flexibility in representation and inherent uncertainty, Gaussian **algorithms** are often used in machine-learning applications. A Gaussian process is built upon fundamental concepts like multivariate normal distribution and non-parametric models kernels, joint, conditional, and other mathematical models.

Gaussian processes regression (GPR), a model that uses prior knowledge (kernels), can make predictions and provide uncertainty measures. This is a supervised learning technique that was developed by the computer science and statistical communities.

Gaussian process regression is nonparametric and therefore not restricted by any functional form. GPR calculates the probability distribution for all functions that can be used to fit the data, rather than calculating the probability distribution for a particular function's parameters.

10. POLYNOMIAL REGRESSION

The polynomial regression is a algorithm which models the relationship between an independent (x) variable and a dependent (y) variable as an nth-degree polynomial. Here is the equation for Polynomial Regression:

y= b0 b1x1 b2x12 b2x13 ...... bnx1n

This is also called the Special Scenario of Multiple Linear Rgression in machine learning. To make it polynomial, several polynomial terms have been added to the Multiple Linear Regression equation. This is a modified linear model to increase accuracy. Non-linear data is used to train in polynomial analysis. To fit complex and non-linear functions and datasets. The original features are transformed into polynomial features of required degree (2,3 n), and then modelled with a linear model.

**CONCLUSION**

These are the most popular algorithms for regression analysis. You should be familiar with all types of regression analysis, no matter how old you are. It is important to identify how many variables you will use to make predictions in ML. A linear regression algorithm in ML is best if you only have one variable to predict.

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