How will you determine the machine learning algorithm that is suitable for your problem explain in one sentence?

We have to choose suitable Machine Learning algorithm depending on the problem statement and the dataset and no model is better algorithm compared to another. If it is unsupervised learning, then use clustering algorithms like K-means algorithm.

Which of the algorithm is used for predicting & classification?

Random Forest is perhaps the most popular classification algorithm, capable of both classification and regression. It can accurately classify large volumes of data. The name “Random Forest” is derived from the fact that the algorithm is a combination of decision trees.

How do you identify machine learning problems?

Identifying Good Problems for ML

1. Start with the problem, not the solution. Make sure you aren’t treating ML as a hammer for your problems.
2. Be prepared to have your assumptions challenged.
3. ML requires a lot of relevant data.
4. Your features contain predictive power.

What are the features required to have a well defined machine learning problem?

A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance in tasks T, as measured by P, improves with experience E.

How does machine learning classification work?

Classification is computed from a simple majority vote of the k nearest neighbors of each point. It is supervised and takes a bunch of labeled points and uses them to label other points. To label a new point, it looks at the labeled points closest to that new point also known as its nearest neighbors.

Why classification is important in machine learning?

A common job of machine learning algorithms is to recognize objects and being able to separate them into categories. This process is called classification, and it helps us segregate vast quantities of data into discrete values, i.e. :distinct, like 0/1, True/False, or a pre-defined output label class.

What is well defined learning problem in machine learning?

A (machine learning) problem is well-posed if a solution to it exists, if that solution is unique, and if that solution depends on the data / experience but it is not sensitive to (reasonably small) changes in the data / experience.

What is descriptive rule learning in machine learning?

Supervised descriptive rule induction (SDRI) is a machine learning task in which individual patterns in the form of rules (see Classification rule) intended for interpretation are induced from data, labeled by a predefined property of interest.