What type of ML algorithm is suitable for predicting the continuous dependent variable with two different values?

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What type of ML algorithm is suitable for predicting the continuous dependent variable?

It is one of the most widely known modeling technique. Linear regression is usually among the first few topics which people pick while learning predictive modeling. In this technique, the dependent variable is continuous, independent variable(s) can be continuous or discrete, and nature of regression line is linear.

Which machine learning algorithm is more applicable for continuous data?

Regression Algorithms are the Machine Learning Algorithms that are more applicable for the analysis of continuous data. Explanation: Regression is a predictive modeling and analyzing technique of continuous data.

Which supervised learning technique is used to predict continuous dependent variables?

Regression is a supervised learning technique which helps in finding the correlation between variables and enables us to predict the continuous output variable based on the one or more predictor variables.

Which algorithm is used for classification?

3.1 Comparison Matrix

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Classification Algorithms Accuracy F1-Score
K-Nearest Neighbours 83.56% 0.5924
Decision Tree 84.23% 0.6308
Random Forest 84.33% 0.6275
Support Vector Machine 84.09% 0.6145

Which algorithm is faster in machine learning?

A Naïve Bayes classifier converges faster, requiring relatively little training data than other discriminative models like logistic regression, when the Naïve Bayes conditional independence assumption holds. With Naïve Bayes Classifier algorithm, it is easier to predict class of the test data set.

Which type of output data is used for classification?

Classification Algorithms are used with discrete data. In Regression, we try to find the best fit line, which can predict the output more accurately. In Classification, we try to find the decision boundary, which can divide the dataset into different classes.

What are the regression techniques?

Below are the different regression techniques:

• Linear Regression.
• Logistic Regression.
• Ridge Regression.
• Lasso Regression.
• Polynomial Regression.
• Bayesian Linear Regression.

What is the key difference between regression and classification?

Classification is the task of predicting a discrete class label. Regression is the task of predicting a continuous quantity.

Can we use regression for classification?

Linear regression is suitable for predicting output that is continuous value, such as predicting the price of a property. … Whereas logistic regression is for classification problems, which predicts a probability range between 0 to 1.

Which algorithm is used for regression?

Linear Regression is an ML algorithm used for supervised learning. Linear regression performs the task to predict a dependent variable(target) based on the given independent variable(s). So, this regression technique finds out a linear relationship between a dependent variable and the other given independent variables.

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Why is multi linear regression such an effective analytic method for supervised learning?

The most important advantage of Multivariate regression is it helps us to understand the relationships among variables present in the dataset. This will further help in understanding the correlation between dependent and independent variables. Multivariate linear regression is a widely used machine learning algorithm.

What are the types of supervised learning?

Different Types of Supervised Learning

• Regression. In regression, a single output value is produced using training data. …
• Classification. It involves grouping the data into classes. …
• Naive Bayesian Model. …
• Random Forest Model. …
• Neural Networks. …
• Support Vector Machines.