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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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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 semi-supervised learning?

  • K-Means Clustering
  • Random Forest
  • Gradient Boosting
  • Naive Bayes
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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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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 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 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 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 sentiment analysis?

  • Decision Trees
  • Support Vector Machines (SVM)
  • Naive Bayes
  • 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 of the following is a kernel-based algorithm?

  • K-Nearest Neighbors (KNN)
  • Decision Trees
  • Random Forest
  • Linear Regression
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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 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 of the following is NOT a supervised learning algorithm?

  • Decision Trees
  • K-Means Clustering
  • Linear Regression
  • Support Vector Machines
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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 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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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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What is the purpose of the term "learning rate" in gradient descent optimization?

  • It determines the number of iterations during training
  • It specifies the size of the steps taken towards the minimum
  • It controls the size of the training dataset
  • It determines the number of features in the dataset
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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 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 natural language processing (NLP) tasks such as text classification?

  • Decision Trees
  • Naive Bayes
  • Naive Bayes
  • Random Forest
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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 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 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 evaluation metric is suitable for classification problems with imbalanced classes?

  • Accuracy
  • Precision
  • Recall
  • F1 Score
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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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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 time series forecasting?

  • Decision Trees
  • K-Means Clustering
  • ARIMA (AutoRegressive Integrated Moving Average)
  • Support Vector Machines (SVM)
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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 kernel function used in Support Vector Machines (SVM)?

  • Linear
  • Polynomial
  • Sigmoid
  • Logarithmic
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