Machine Learning - Overview - Type Of Machine Learning Qna

Machine Learning is a field of computer science that uses statistical techniques to give computer system the ability to “learn” with data, without being explicitly programmed

Multiple way to avoid overfitting is - reguralization, bagging and boosting.
Making a simple model with lesser variable & parameter.
Using Cross validation method like k-fold

The training set is data given to the model to analyze and learn and make computation.
The test set is a data to test the accuracy of the hypothesis generated by the model

When the training set is small, a model that has a high bias and low variance seems to work better because they are less likely to overfit.
an algorithm with high bias is Linear Regression, Linear Discriminant Analysis and Logistic Regression.

For example, Naive Bayes works best when the training set is large. Models with low bias and high variance tend to perform better as they work fine with complex relationships.
Machine learning algorithms with low bias are Decision Trees, k-Nearest Neighbours and Support Vector Machines.

In the case of semi-supervised learning, the training data contains a small amount of labeled data and a large amount of unlabeled data.

The classifier is called ‘naive’ because it makes assumptions that may or may not be correct.
The algorithm assumes that the presence of one feature of a class is not related to the presence of any other feature.
For instance, a fruit may be considered to be a cherry if it is red in color and round in shape, regardless of other features. This assumption may or may not be right (as an apple also matches the description).
 

While there is no fixed rule to choose an algorithm for a classification problem, you can follow these guidelines:
• If accuracy is a concern, test different algorithms and cross-validate them
• If the training dataset is small, use models that have low variance and high bias
• If the training dataset is large, use models that have high variance and low bias

There is no master algorithm for all situations. Choosing an algorithm depends on the following questions:
• How much data do you have, and is it continuous or categorical?
• Is the problem related to classification, association, clustering, or regression?
• Predefined variables (labeled), unlabeled, or mix?
• What is the goal?