Quiz | Croma Campus

What is the primary objective of feature scaling in machine learning?

To improve model interpretability
To reduce the number of features in the dataset
To ensure that features have the same scale
To handle missing values in the dataset

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Which of the following is a clustering algorithm?

Linear Regression
K-Means Clustering
Random Forest
Gradient Boosting

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What is the purpose of the term "momentum" in gradient descent optimization?

It controls the size of the steps taken towards the minimum
It determines the number of iterations during training
It helps accelerate convergence by adding a fraction of the previous update vector
It specifies the size of the training dataset

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What is the purpose of the bias term in a neural network?

To help the model converge faster
To reduce overfitting
To capture the intercept term
To introduce non-linearity

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What is the primary advantage of using a non-linear activation function in a neural network?

Faster convergence of the model
Reduced computational complexity
Ability to capture complex patterns in the data
Improved interpretability of the model

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Which algorithm is used for density estimation?

Decision Trees
K-Means Clustering
Gaussian Mixture Models (GMM)
Support Vector Machines (SVM)

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Which algorithm is used for anomaly detection in a network?

K-Means Clustering
K-Nearest Neighbors (KNN)
Isolation Forest
Random Forest

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Which algorithm is used for sentiment analysis?

Decision Trees
Support Vector Machines (SVM)
Naive Bayes
Linear Regression

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Which evaluation metric is preferred when there is a high cost associated with false negatives?

Precision
Recall
F1 Score
Accuracy

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Which algorithm is used for sequence generation tasks such as text generation?

Long Short-Term Memory (LSTM)
Random Forest
K-Means Clustering
Linear Regression

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What is the primary difference between bagging and boosting ensemble techniques?

Bagging trains multiple models sequentially, while boosting trains them simultaneously
Bagging combines predictions from multiple models, while boosting trains models iteratively
Bagging trains multiple models on different subsets of data, while boosting trains models sequentially
Bagging reduces the variance of a model, while boosting reduces bias

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In machine learning, what does "overfitting" refer to?

Model performs well on training data but poorly on unseen data
Model performs poorly on both training and unseen data
Model fits noise in the training data rather than the underlying pattern
Model's inability to capture the underlying pattern in the data

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What is the primary advantage of using dropout regularization in neural networks?

Helps reduce bias in the model
Prevents overfitting by randomly dropping neurons during training
Increases the learning rate
Speeds up the convergence of the model

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What is the purpose of the term "early stopping" in neural network training?

To prevent the model from converging too quickly
To stop training when the validation error starts increasing
To initialize the weights of the model
To adjust the learning rate during training

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Which algorithm is used for community detection in graphs?

K-Means Clustering
PageRank
Decision Trees
Linear Regression

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Which of the following is a kernel-based algorithm?

K-Nearest Neighbors (KNN)
Decision Trees
Random Forest
Linear Regression

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Which technique is used to preprocess text data by converting words into their base forms?

Lemmatization
Tokenization
Stemming
Bag of Words

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What is the purpose of the term "cross-validation" in machine learning?

To estimate the performance of a model on unseen data
To optimize hyperparameters
To prevent overfitting
To evaluate model performance on training data

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Which technique is used to address the class imbalance problem in classification tasks?

Feature Scaling
Data Augmentation
SMOTE (Synthetic Minority Over-sampling Technique)
Regularization

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Which evaluation metric is suitable for classification problems with imbalanced classes?

Accuracy
Precision
Recall
F1 Score

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Which algorithm is used for natural language processing (NLP) tasks such as text classification?

Decision Trees
Naive Bayes
Naive Bayes
Random Forest

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Which technique is used to preprocess categorical variables in a dataset?

Label Encoding
Feature Scaling
One-Hot Encoding
Imputation

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Which of the following is a regularization technique used to prevent overfitting in neural networks?

Gradient Descent
Dropout
Batch Normalization
Learning Rate Decay

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Which of the following is NOT a kernel function used in Support Vector Machines (SVM)?

Linear
Polynomial
Sigmoid
Logarithmic

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Which of the following is a distance-based algorithm?

Decision Trees
K-Means Clustering
Random Forest
Gradient Boosting

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Which of the following is NOT a supervised learning algorithm?

Decision Trees
K-Means Clustering
Linear Regression
Support Vector Machines

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What is the main drawback of the K-Means Clustering algorithm?

Sensitivity to the initialization of cluster centroids
Inability to handle high-dimensional data
Requires labeled data for training
Not suitable for large datasets

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Which algorithm is used for unsupervised learning?

Linear Regression
Support Vector Machines (SVM)
K-Means Clustering
Random Forest

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What is the purpose of the activation function in a neural network?

To determine the learning rate
To compute the gradient
To introduce non-linearity
To initialize the weights

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Which algorithm is used for regression tasks with a large number of features?

Linear Regression
Ridge Regression
Lasso Regression
Decision Trees

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