Quiz | Croma Campus

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 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 technique is used to visualize high-dimensional data in a lower-dimensional space?

Principal Component Analysis (PCA)
Support Vector Machines (SVM)
K-Means Clustering
Decision Trees

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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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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 unsupervised learning?

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

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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 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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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 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 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 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 supervised learning algorithm?

Decision Trees
K-Means Clustering
Linear Regression
Support Vector Machines

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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 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 regularization technique used to prevent overfitting in neural networks?

Gradient Descent
Dropout
Batch Normalization
Learning Rate Decay

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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 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 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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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 anomaly detection?

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

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Which algorithm is used for recommendation systems?

K-Means Clustering
Apriori Algorithm
PageRank
Support Vector Machines (SVM)

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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 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 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 anomaly detection in a network?

K-Means Clustering
K-Nearest Neighbors (KNN)
Isolation Forest
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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Which algorithm is used for semi-supervised learning?

K-Means Clustering
Random Forest
Gradient Boosting
Naive Bayes

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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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Which algorithm is used for sentiment analysis?

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

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