Which machine learning algorithm is 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 machine learning algorithm is best for binary classification?

Popular algorithms that can be used for binary classification include:

• Logistic Regression.
• k-Nearest Neighbors.
• Decision Trees.
• Support Vector Machine.
• Naive Bayes.

What are the 4 types of data that machine learning can use?

Most data can be categorized into 4 basic types from a Machine Learning perspective: numerical data, categorical data, time-series data, and text.

What are the types of machine learning algorithms?

There are four types of machine learning algorithms: supervised, semi-supervised, unsupervised and reinforcement.

What is Q learning algorithm in machine learning?

Q-learning is a model-free reinforcement learning algorithm to learn the value of an action in a particular state. “Q” refers to the function that the algorithm computes – the expected rewards for an action taken in a given state.

Which classifier is best in machine learning?

Decision Tree. The decision tree is one of the most popular machine learning algorithms used. They are used for both classification and regression problems. Decision trees mimic human-level thinking so it’s so simple to understand the data and make some good intuitions and interpretations.

What is machine learning algorithm?

A machine learning algorithm is the method by which the AI system conducts its task, generally predicting output values from given input data. The two main processes of machine learning algorithms are classification and regression. Both systems are commonly used in situations where labeled data is scant.

What is ML and its application?

Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so.