Quiz | Croma Campus

What is the goal of feature extraction in data preprocessing?

To transform categorical features into numerical features.
To select the most relevant features for the model.
To create new features based on existing ones.
To handle missing values in the dataset.

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What is the main goal of exploratory data analysis (EDA)?

Building predictive models.
Cleaning and preprocessing data.
Making inferences about a population.
Summarizing and visualizing data to uncover insights.

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What is the primary goal of a content-based recommendation system?

To suggest items based on user preferences.
To analyze social connections and recommend items.
To identify the most popular items and recommend them.
To suggest items similar to those a user has liked in the past.

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In the context of decision trees, what is "entropy" used for?

To measure the number of leaves in the tree.
To calculate the impurity of a node's class distribution.
To determine the maximum depth of the tree.
To estimate the computational complexity of tree construction.

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What is an ROC curve used for in machine learning?

To visualize the distribution of a dataset.
To compare the performance of different models.
To evaluate the convergence of gradient descent.
To measure the amount of overfitting in a model.

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What is the goal of hyperparameter tuning in machine learning?

To increase the complexity of the model.
To reduce the number of features in the dataset.
To optimize the model's performance by selecting the best hyperparameters.
To eliminate noise from the data.

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In a confusion matrix, which term represents the number of true positive predictions?

True Positive (TP)
False Positive (FP)
True Negative (TN)
False Negative (FN)

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What is the "curse of dimensionality" in machine learning?

The difficulty of choosing the right hyperparameters for a model.
The exponential increase in computational complexity as the number of features increases.
The tendency of models to overfit when trained on high-dimensional data.
The challenge of handling imbalanced datasets.

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What is the purpose of regularization in machine learning algorithms?

To make the model more complex.
To make the model less interpretable.
To penalize large coefficient values and prevent overfitting.
To increase the training accuracy of the model.

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What is the purpose of a learning curve in machine learning?

To visualize the performance of a model on a validation set.
To plot the relationship between input features and target values.
To analyze the distribution of the dataset.
To show how a model's performance changes with the size of the training dataset.

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What is the purpose of regularization in machine learning algorithms?

To make the model more complex.
To make the model less interpretable.
To penalize large coefficient values and prevent overfitting.
To increase the training accuracy of the model.

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What is the primary goal of a support vector machine (SVM) algorithm?

To minimize the distance between data points and the decision boundary.
To find the best-fit line through the data points.
To maximize the margin between different classes in the data.
To reduce the variance of the model.

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Which algorithm is used to update the weights of a neural network during the training process?

Gradient Descent
K-Means Clustering
Random Forest
Naive Bayes

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What is the main goal of exploratory data analysis (EDA)?

Building predictive models.
Cleaning and preprocessing data.
Making inferences about a population.
Summarizing and visualizing data to uncover insights.

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Which algorithm is used to update the weights of a neural network during the training process?

Gradient Descent
K-Means Clustering
Random Forest
Naive Bayes

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

To split the dataset into training and testing sets.
To assess the performance of a model on unseen data.
To reduce overfitting by penalizing complex models.
To calculate the gradient descent in optimization algorithms.

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Which technique is used to reduce the impact of outliers in a dataset?

Normalization
Feature Scaling
Outlier Detection
Data Imputation

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Which algorithm is used to update the weights of a neural network during the training process?

Gradient Descent
K-Means Clustering
Random Forest
Naive Bayes

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In the context of deep learning, what is a "dropout" layer used for?

Adding noise to the input data.
Reducing the learning rate during training.
Regularizing the network to prevent overfitting.
Adjusting the weights of the neural network based on errors.

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What is the main difference between supervised and unsupervised learning?

Supervised learning requires labeled data, while unsupervised learning does not.
Unsupervised learning requires labeled data, while supervised learning does not.
Supervised learning uses more complex algorithms than unsupervised learning.
Unsupervised learning is only used for classification tasks.

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

To determine the learning rate of the network.
To transform the input data into a different representation.
To control the number of layers in the network.
To introduce non-linearity to the model.

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In the context of clustering, what is the purpose of the "elbow method"?

To calculate the distance between data points.
To determine the optimal number of clusters.
To evaluate the performance of a clustering algorithm.
To identify outliers in the dataset.

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Which algorithm is often used for natural language processing tasks such as language translation?

Random Forest
Support Vector Machine (SVM)
Recurrent Neural Network (RNN)
K-Means Clustering

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Which technique is used to handle class imbalance in a classification problem?

Regularization
Feature Scaling
Oversampling the minority class
Data Imputation

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What is the purpose of a learning curve in machine learning?

To visualize the performance of a model on a validation set.
To plot the relationship between input features and target values.
To analyze the distribution of the dataset.
To show how a model's performance changes with the size of the training dataset.

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

To split the dataset into training and testing sets.
To assess the performance of a model on unseen data.
To reduce overfitting by penalizing complex models.
To calculate the gradient descent in optimization algorithms.

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Which technique is used to reduce the complexity and size of a model while maintaining its predictive power?

Feature Engineering
Regularization
Clustering
Dimensionality Reduction

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What is the purpose of regularization in machine learning algorithms?

To make the model more complex.
To make the model less interpretable.
To penalize large coefficient values and prevent overfitting.
To increase the training accuracy of the model.

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What is the primary purpose of a hash function in data processing?

To sort data in ascending order.
To transform data into a unique fixed-size value.
To remove duplicates from a dataset.
To compute the mean value of a set of numbers.

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What is the main drawback of the K-Nearest Neighbors (KNN) algorithm?

It is computationally expensive during training.
It cannot handle categorical data.
It requires a labeled dataset for training.
It is sensitive to the choice of the number of neighbors (k).

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