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Overfitting & Underfitting in Machine Learning

Overfitting in Machine Learning

Overfitting refers to a model that models the training data too well.

Overfitting happens when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data. This means that the noise or random fluctuations in the training data is picked up and learned as concepts by the model. The problem is that these concepts do not apply to new data and negatively impact the models ability to generalize.

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1. Which one of these is a Bower Concept?

Package

2. The following is the syntax of installing a Component in Bower.

Bower install componenet name

3. In Package.json, to include the latest version we can use.

Symbol

4. Grunt runs tasks using Temporary files which are disk I/O operations.

TRUE

1. Wireframes are static.

True

2. What are the fidelity levels that you must consider while creating a wireframe?

all the options

3. Mockups exhibit exhibit ___________.

high fidelity

4. Apart from basic wireframes, what are the other type of wireframes that are available?

ALL OF THE OPTION

1. Which of the following is not a in-built Ionic template.

NAVIGATION

2. Which of the following files has the ionic styles?

ionic css

3. Which of the following files contains all required JS files for Ionic and Angular?

ionic.bundle.min.js

4. Which of the following folders contains icons in Ionic?

fonts

1. What is the support for Screen Guard ?

0.8

2. What is the support for Power Bank ?

0.6

3. What is the confidence for Screen Guard -> Mobile Cover ?

0.5

4. What is the confidence for Power Bank -> Mobile Cover ?

.33

Machine Learning - Exploring the Model

1. What are the features of multivariate random variable?

BOTH

2. If time space or state space is discrete, ___________.

Markov process can be termed as discrete-time Markov chains

3. What are kernels?

Small