Quiz | Croma Campus

Which algorithm is used for anomaly detection?

K-Means Clustering
K-Nearest Neighbors
Isolation Forest
Decision Trees

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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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Which algorithm is used for dimensionality reduction while preserving the pairwise distances between data points?

Linear Discriminant Analysis (LDA)
t-Distributed Stochastic Neighbor Embedding (t-SNE)
Principal Component Analysis (PCA)
Singular Value Decomposition (SVD)

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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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What is the purpose of the term "stride" in convolutional neural networks (CNNs)?

It controls the size of the filters applied to the input
It determines the number of layers in the network
It specifies the size of the steps taken while sliding the filters over the input
It helps prevent overfitting in the model

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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 evaluation metric is suitable for classification problems with imbalanced classes?

Accuracy
Precision
Recall
F1 Score

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What does the term "bag of words" represent in natural language processing?

A technique for representing text data as vectors
A method for stemming words in a document
An algorithm for text classification
A model for sequential data

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Which of the following algorithms is NOT suitable for handling text data?

Naive Bayes
Logistic Regression
K-Means Clustering
Recurrent Neural Networks (RNN)

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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 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 primary advantage of using convolutional neural networks (CNNs) for image classification?

Ability to handle sequential data efficiently
Capability to automatically learn hierarchical patterns
Less prone to overfitting compared to other models
Suitable for handling high-dimensional data

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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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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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What is the purpose of the term "dropout" in neural networks?

To randomly remove a fraction of neurons during training
To increase the learning rate
To introduce non-linearity
To regularize the model

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Which algorithm is used for dimensionality reduction?

K-Nearest Neighbors
Random Forest
Principal Component Analysis (PCA)
Gradient Boosting

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Which technique is used for handling missing data in a dataset?

Data Augmentation
Feature Scaling
Imputation
Regularization

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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 of the following is NOT a hyperparameter for decision trees?

Maximum Depth
Minimum Samples Split
Learning Rate
Criterion

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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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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 goal of ensemble learning?

To train multiple models independently
To combine predictions from multiple models
To reduce the complexity of a single model
To speed up the training process

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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 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 semi-supervised learning?

K-Means Clustering
Random Forest
Gradient Boosting
Naive Bayes

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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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Which algorithm is used for sentiment analysis?

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

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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 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 a hyperparameter for the K-Nearest Neighbors (KNN) algorithm?

Number of clusters
Learning rate
Number of neighbors (K)
Activation function

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