Erode Quiz ML

(a)   is an ensemble learning technique that improves the performance of a machine learning model by sequentially training weak models, with each new model focusing on correcting the errors made by the previous models.

If we didn't assign a base estimators to the bagging classifier it will use by default:

• Linear regression

• Decision tree

• KNN

• Logistic regression

What is meant by the number of estimators:

• The number of base estimators in the ensemble.

• The number of features

• The number of accuracies

• The number of data rows

A value that defines the step taken at each iteration, before correction?

• Gradient descent

• learning rate

• l2 regularization

• l1 regularization

Which of these is not a supervised learning algorithm?

• Representation Learning

• Classification

• Regression

Which of the following methods do we use to find the best fit line for data in Linear Regression?

• Least Square Error

• Maximum Likelihood

• Logarithmic Loss

• Both A and B

_____ is a learning model that is used to identify a relationship between large amounts of information from a data set.

• Classification

• Multi class classification

• Association

• Unsupervised learning

The goal of clustering a set of data is to?

• choose the best data from the set

• divide them into groups of data that are near each other

• determine the nearest neighbors of each of the data

• predict the class of data

Agglomerative hierarchical clustering is also known as

• Top down approach

• Bottom up approach

• Both of these

• None of these

In unsupervised learning, the machine learns by

• Identifying patterns in the dataset

• Using class labels

• None of these

• Both of these