Quiz | Croma Campus

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

Linear
Polynomial
Sigmoid
Logarithmic

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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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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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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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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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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 time series forecasting?

Decision Trees
K-Means Clustering
ARIMA (AutoRegressive Integrated Moving Average)
Support Vector Machines (SVM)

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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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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 text data by converting words into their base forms?

Lemmatization
Tokenization
Stemming
Bag of Words

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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 of the following is a distance-based algorithm?

Decision Trees
K-Means Clustering
Random Forest
Gradient Boosting

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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 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 technique is used to preprocess categorical variables in a dataset?

Label Encoding
Feature Scaling
One-Hot Encoding
Imputation

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What is the primary purpose of a validation set in machine learning?

To evaluate the model's performance on unseen data
To train the model on a larger dataset
To fine-tune hyperparameters using grid search
To measure the model's training error

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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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What is the purpose of the term "batch size" in neural network training?

It determines the number of layers in the network
It controls the rate at which the model learns
It specifies the number of samples processed before updating the model's parameters
It determines the number of epochs during training

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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 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 a kernel-based algorithm?

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

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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 clustering algorithm?

Linear Regression
K-Means Clustering
Random Forest
Gradient Boosting

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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 NOT a hyperparameter for decision trees?

Maximum Depth
Minimum Samples Split
Learning Rate
Criterion

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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 semi-supervised learning?

K-Means Clustering
Random Forest
Gradient Boosting
Naive Bayes

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Which algorithm is used for anomaly detection?

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

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

Precision
Recall
F1 Score
Accuracy

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