DSA-C02 Online Practice Questions and Answers

Correct Answer: C

Explanation:

What is Feature Engineering?

Feature engineering is the process of transforming raw data into features that are suitable for ma-chine learning models. In other words, it is the process of selecting, extracting, and transforming the most relevant features from the available

data to build more accurate and efficient machine learning models.

The success of machine learning models heavily depends on the quality of the features used to train them. Feature engineering involves a set of techniques that enable us to create new features by combining or transforming the existing

ones. These techniques help to highlight the most important pat-terns and relationships in the data, which in turn helps the machine learning model to learn from the data more effectively.

What is a Feature?

In the context of machine learning, a feature (also known as a variable or attribute) is an individual measurable property or characteristic of a data point that is used as input for a machine learning al-gorithm. Features can be numerical,

categorical, or text-based, and they represent different aspects of the data that are relevant to the problem at hand. For example, in a dataset of housing prices, features could include the number of bedrooms, the square footage, the location,

and the age of the property. In a dataset of customer demographics, features could include age, gender, income level, and occupation. The choice and quality of features are critical in machine learning, as they can greatly impact the accuracy and performance of the model.

Why do we Engineer Features?

We engineer features to improve the performance of machine learning models by providing them with relevant and informative input data. Raw data may contain noise, irrelevant information, or missing values, which can lead to inaccurate or

biased model predictions. By engineering features, we can extract meaningful information from the raw data, create new variables that capture important patterns and relationships, and transform the data into a more suitable format for

machine learning algorithms. Feature engineering can also help in addressing issues such as overfitting, underfitting, and high di-mensionality. For example, by reducing the number of features, we can prevent the model from be-coming too

complex or overfitting to the training data. By selecting the most relevant features, we can improve the model's accuracy and interpretability. In addition, feature engineering is a crucial step in preparing data for analysis and decision- making

in various fields, such as finance, healthcare, marketing, and social sciences. It can help uncover hidden insights, identify trends and patterns, and support data-driven decision-making.

We engineer features for various reasons, and some of the main reasons include:

Improve User Experience: The primary reason we engineer features is to enhance the user experience of a product or service. By adding new features, we can make the product more intuitive, efficient, and user-friendly, which can increase

user satisfaction and engagement. Competitive Advantage: Another reason we engineer features is to gain a competitive advantage in the marketplace. By offering unique and innovative features, we can differentiate our product from

competitors and attract more customers. Meet Customer Needs: We engineer features to meet the evolving needs of customers. By analyzing user feedback, market trends, and customer behavior, we can identify areas where new features

could enhance the product's value and meet customer needs. Increase Revenue: Features can also be engineered to generate more revenue. For example, a new feature that streamlines the checkout process can increase sales, or a feature

that provides additional functionality could lead to more upsells or cross-sells. Future-Proofing: Engineering features can also be done to future-proof a product or service. By an-ticipating future trends and potential customer needs, we can

develop features that ensure the product remains relevant and useful in the long term.

Processes Involved in Feature Engineering

Feature engineering in Machine learning consists of mainly 5 processes: Feature Creation, Feature Transformation, Feature Extraction, Feature Selection, and Feature Scaling. It is an iterative process that requires experimentation and

testing to find the best combination of features for a given problem. The success of a machine learning model largely depends on the quality of the features used in the model.

Feature Transformation

Feature Transformation is the process of transforming the featuresinto a more suitable representation for the machine learning model. This is done to ensure that the model can effectively learn from the data.

Types of Feature Transformation:

Normalization: Rescaling the features to have a similar range, such as between 0 and 1, to prevent some features from dominating others.

Scaling: Rescaling the features to have a similar scale, such as having a standard deviation of 1, to make sure the model considers all features equally. Encoding: Transforming categorical features into a numerical representation. Examples

are one-hot encoding and label encoding.

Transformation: Transforming the features using mathematical operations to change the distribution or scale of the features. Examples are logarithmic, square root, and reciprocal transformations.

Correct Answer: D

Correct Answer: D

Explanation:

The drop_duplicates() method removes duplicate rows. dataframe.drop_duplicates(subset, keep, inplace, ignore_index) Remove duplicate rows from the DataFrame:

1.import pandas as pd

2.data = {

3."name": ["Peter", "Mary", "John", "Mary"],

4."age": [50, 40, 30, 40],

5."qualified": [True, False, False, False]

6.}

7.

8.df = pd.DataFrame(data)

9.newdf = df.drop_duplicates()

Correct Answer: C

Explanation:

Computes length of column A and Sum of Column B values of each group

Correct Answer: C

Explanation:

Partial dependence plots (PDP) is a useful tool for gaining insights into the relationship between features and predictions. It helps us understand how different values of a particular feature impact model's predictions.

Correct Answer: B

Explanation:

Hyperparameters in Machine learning are those parameters that are explicitly defined by the user to control the learning process. These hyperparameters are used to improve the learning of the model, and their values are set before starting

the learning process of the model.

What are hyperparameters?

In Machine Learning/Deep Learning, a model is represented by its parameters. In contrast, a training process involves selecting the best/optimal hyperparameters that are used by learning algorithms to provide the best result. So, what are

these hyperparameters? The answer is, "Hyperparameters are defined as the parameters that are explicitly defined by the user to control the learning process."

Here the prefix "hyper" suggests that the parameters are top-level parameters that are used in con-trolling the learning process. The value of the Hyperparameter is selected and set by the machine learning engineer before the learning

algorithm begins training the model. Hence, these are external to the model, and their values cannot be changed during the training process.

Some examples of Hyperparameters in Machine Learning ?The k in kNN or K-Nearest Neighbour algorithm

Learning rate for training a neural network

Train-test split ratio

Batch Size

Number of Epochs

Branches in Decision Tree

Number of clusters in Clustering Algorithm

Model Parameters:

Model parameters are configuration variables that are internal to the model, and a model learns them on its own. For example, W Weights or Coefficients of independentvariables in the Linear regression model. or Weights or Coefficients of

independent variables in SVM, weight, and biases of a neural network, cluster centroid in clustering. Some key points for model parameters are as follows:

They are used by the model for making predictions.

They are learned by the model from the data itself.

These are usually not set manually.

These are the part of the model and key to a machine learning Algorithm.

Model Hyperparameters:

Hyperparameters are those parameters that are explicitly defined by the user to control the learning process. Some key points for model parameters are as follows:

These are usually defined manually by the machine learning engineer. One cannot know the exact best value for hyperparameters for the given problem. The best value can be determined either by the rule of thumb or by trial and error. Some

examples of Hyperparameters are the learning rate for training a neural network, K in the KNN algorithm.

Correct Answer: CD

Explanation:

Window Functions

A window function operates on a group ("window") of related rows. Each time a window function is called, it is passed a row (the current row in the window) and the window of rows that contain the current row. The window function returns one

output row for each input row. The output depends on the individual row passed to the function and the values of the other rows in the window passed to the function. Some window functions are order-sensitive. There are two main types of

order-sensitive window functions:

Rank-related functions.

Window frame functions.

Rank-related functions list information based on the "rank" of a row. For example, if you rank stores in descending order by profit per year, the store with the most profit will be ranked 1; the second-most profitable store will be ranked 2, etc.

Window frame functions allow you to perform rolling operations, such as calculating a running total or a moving average, on a subset of the rows in the window.

Correct Answer: C

Explanation:

Holdout cross-validation method is suitable for very large dataset because it is the simplest and quicker to compute version of cross-validation.

Holdout method

In this method, the dataset is divided into two sets namely the training and the test set with the basic property that the training set is bigger than the test set. Later, the model is trained on the training dataset and evaluated using the test

dataset.

Correct Answer: C

Explanation:

Since the normal curve is symmetric about its mean, its skewness is zero. This is a theoretical explanation for mathematical proofs, you can refer to books or websites that speak on the same in detail.

Correct Answer: D