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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 density estimation?

  • Decision Trees
  • K-Means Clustering
  • Gaussian Mixture Models (GMM)
  • Support Vector Machines (SVM)
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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 unsupervised learning?

  • Linear Regression
  • Support Vector Machines (SVM)
  • K-Means Clustering
  • Random Forest
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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 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 for handling missing data in a dataset?

  • Data Augmentation
  • Feature Scaling
  • Imputation
  • Regularization
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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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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 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 is NOT a supervised learning algorithm?

  • Decision Trees
  • K-Means Clustering
  • Linear Regression
  • Support Vector Machines
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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 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 algorithm is used for anomaly detection?

  • K-Means Clustering
  • K-Nearest Neighbors
  • Isolation Forest
  • Decision Trees
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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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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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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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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 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 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 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 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 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 algorithms is NOT suitable for handling text data?

  • Naive Bayes
  • Logistic Regression
  • K-Means Clustering
  • Recurrent Neural Networks (RNN)
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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 algorithm is used for community detection in graphs?

  • K-Means Clustering
  • PageRank
  • Decision Trees
  • Linear Regression
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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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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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