Supervised Learning Basics Coding Practice & MCQs
Practice the latest Supervised Learning Basics technical interview questions. Includes multiple choice questions across various difficulty levels.
Q1: What is the primary goal of supervised learning?
easy- Unsupervised learning
- Generative model
- Predictive model
- Reinforcement learning
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Q2: What is the key concept in supervised learning?
easy- Loss function
- Activation function
- Backpropagation
- Optimization algorithm
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Q3: What type of problem is regression?
easy- Classification
- Clustering
- Regression
- Dimensionality reduction
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Q4: What is a common loss function used for binary classification?
medium- Mean squared error
- Cross-entropy loss
- Mean absolute error
- Mean squared logarithmic error
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Q5: What is the role of the activation function in a neural network?
medium- To introduce non-linearity
- To reduce dimensionality
- To improve generalization
- To speed up computation
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Q6: What is the purpose of backpropagation in a neural network?
hard- To compute the output of the network
- To compute the gradients of the loss function
- To update the model weights
- To optimize the learning rate
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Q7: What is stochastic gradient descent (SGD) in supervised learning?
medium- An optimization algorithm
- A loss function
- A regularization technique
- A neural network architecture
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Q8: What is the difference between batch gradient descent and stochastic gradient descent?
hard- Batch size
- Learning rate
- Number of iterations
- Optimization algorithm
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Q9: What is the purpose of cross-validation in supervised learning?
medium- To evaluate model performance
- To tune hyperparameters
- To select the best model
- To reduce overfitting
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Q10: What is the difference between precision and recall in classification?
hard- Precision measures true positives
- Recall measures true positives
- Precision measures true negatives
- Recall measures false positives
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Q11: What is the role of the learning rate in a neural network?
medium- To introduce non-linearity
- To reduce dimensionality
- To improve generalization
- To update model weights
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Q12: What is the difference between a decision tree and a random forest?
hard- Number of decision nodes
- Number of features used
- Type of decision node
- Number of trees used
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Q13: What is the role of the activation function in a neural network?
medium- To introduce non-linearity
- To reduce dimensionality
- To improve generalization
- To speed up computation
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Q14: What is the difference between a linear regression model and a logistic regression model?
hard- Output type
- Activation function
- Loss function
- Optimization algorithm
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Q15: What is the purpose of hyperparameter tuning in supervised learning?
medium- To evaluate model performance
- To tune model weights
- To select the best model
- To optimize hyperparameters
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Q16: What is the role of regularization in a neural network?
medium- To introduce non-linearity
- To reduce dimensionality
- To improve generalization
- To penalize large model weights
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Q17: What is the purpose of feature scaling in supervised learning?
medium- To reduce overfitting
- To improve generalization
- To speed up computation
- To standardize feature values
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Q18: What is regularization in supervised learning?
medium- Adding noise to the data
- Adding complexity to the model
- Penalizing large model weights
- Increasing the learning rate
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Q19: What is the difference between a perceptron and a multilayer perceptron?
hard- Number of inputs
- Number of layers
- Activation function
- Learning algorithm
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Q20: What is overfitting in supervised learning?
easy- When the model is too simple
- When the model is too complex
- When the model generalizes well
- When the model is too slow
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