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NeuroData Training Quiz 3

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NeuroData Training Quiz

Test your knowledge on machine learning concepts with our engaging NeuroData Training Quiz. Designed for both beginners and aficionados, this quiz covers fundamental topics in data science and neural networks.

10 Multiple Choice Questions
Assess your understanding of key machine learning principles
Instant feedback on your performance

10 Questions2 MinutesCreated by LearningTree472

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

Gradient descent

learning rate

L2 regularization

L1 regularization

Adding a new feature to the model always results in equal or better performance on the training set?

True

False

Which of the statement about gradient descent is true?

We find local maxima in gradient descent

It is an optimization algorithm

Which of these metrics are used to evaluate classification algorithm?

AUC

Precision

F1 Score

Predicted vs True Chart

Which of these can be evaluated by a confusion matrix ?

F1 Score

Recall

Precision

All

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

. What are the key challenges in successfully training machine learning model?

Features available in the data sets

Algorithms that are suitable for the task

Hyper parameters Tuning

Evaluation Metrics

_____ 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

The following descriptions best describe what: 1. Value that has to be assigned manually. 2. The K value in K-nearest-neighbor is an example of this. 3. Value is set before the training.

Centroid

Model Parameter

Model hyper parameter

Significance

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