Q&A Time
Machine Learning

No Short Description Present

Course Related Question

# Question
Q1
Discuss
Type Of Machine Learning
What is machine learning?
S1.

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


Q2
Discuss
Type Of Machine Learning
What is Overfitting, and How Can You Avoid It?
S1.

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


Q3
Discuss
Type Of Machine Learning
What is ‘training Set’ and ‘test Set’ in a Machine Learning Model? How Much Data Will You Allocate for Your Training, Validation, and Test Sets?
S1.

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


Q4
Discuss
Type Of Machine Learning
How Can You Choose a Classifier Based on a Training Set Data Size?
S1.

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.


Q5
Discuss
Type Of Machine Learning
What is Semi-supervised Machine Learning?
S1.

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


Q6
Discuss
Type Of Machine Learning
What Is ‘naive’ in the Naive Bayes Classifier?
S1.

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).

 


Q7
Discuss
Type Of Machine Learning
How Will You Know Which Machine Learning Algorithm to Choose for Your Classification Problem?
S1.

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


Q8
Discuss
Type Of Machine Learning
Considering a Long List of Machine Learning Algorithms, given a Data Set, How Do You Decide Which One to Use?
S1.

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?

 


# Question