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 evaluation metric is suitable for classification problems with imbalanced classes?

Accuracy
Precision
Recall
F1 Score

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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 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 "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 of the following is a distance-based algorithm?

Decision Trees
K-Means Clustering
Random Forest
Gradient Boosting

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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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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 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 algorithm is used for unsupervised learning?

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

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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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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 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 does the term "precision" measure in a classification problem?

The ratio of true positives to the sum of true positives and false negatives
The ratio of true positives to the sum of true positives and false positives
The ratio of true negatives to the sum of true negatives and false positives
The ratio of true negatives to the sum of true negatives and false negatives

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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 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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Which algorithm is used for recommendation systems?

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

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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 sentiment analysis?

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

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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 algorithm is used for community detection in graphs?

K-Means Clustering
PageRank
Decision Trees
Linear Regression

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

Precision
Recall
F1 Score
Accuracy

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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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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 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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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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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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