Which machine learning algorithm should you use by problem type?

Naive Bayes, SVM , Multilayer Perceptron Neural Networks (MLPNNs) and Radial Base Function Neural Networks (RBFNN) suggested.

What machine learning model should I use?

When most dependent variables are numeric, logistic regression and SVM should be the first try for classification. These models are easy to implement, their parameters easy to tune, and the performances are also pretty good. So these models are appropriate for beginners.

What are the criteria to choose the best algorithm for a problem class 11?

(A) Characteristics of a good algorithm Finiteness — the algorithm always stops after a finite number of steps. Input — the algorithm receives some input. Output — the algorithm produces some output.

Which algorithm is different machine learning?

Broadly, there are 3 types of Machine Learning Algorithms Examples of Supervised Learning: Regression, Decision Tree, Random Forest, KNN, Logistic Regression etc.

Which of the following algorithm is mostly used in classification problems of machine learning with Python?

Decision Tree This is one of my favorite algorithm and I use it quite frequently. It is a type of supervised learning algorithm that is mostly used for classification problems.

What are the basic criteria for selecting appropriate algorithm?

All algorithms must satisfy the following criteria:

• Zero or more input values.
• One or more output values.
• Clear and unambiguous instructions.
• Atomic steps that take constant time.
• No infinite sequence of steps (help, the halting problem)
• Feasible with specified computational device.

What are the criteria used to identify the best algorithm?

Input: a good algorithm must be able to accept a set of defined input. Output: a good algorithm should be able to produce results as output, preferably solutions. Finiteness: the algorithm should have a stop after a certain number of instructions. Generality: the algorithm must apply to a set of defined inputs.