Machine learning common interview questions

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  1. What is machine learning? a) A field of computer science that uses algorithms and statistical models to enable a system to improve its performance on a specific task through experience. - Answer b) A field of computer science that uses algorithms and mathematical models to solve problems. c) A field of computer science that uses only algorithms to solve problems. d) A field of computer science that uses only mathematical models to solve problems.

  2. What are the types of machine learning? a) Supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning - Answer b) Predictive learning, descriptive learning, prescriptive learning, reinforcement learning c) Regression learning, classification learning, clustering learning, reinforcement learning d) Supervised learning, unsupervised learning, reinforcement learning, deep learning

  3. What is supervised learning? a) A type of machine learning where the algorithm is trained using labeled data to make predictions on new, unseen data. - Answer b) A type of machine learning where the algorithm is trained using only unlabeled data. c) A type of machine learning where the algorithm is not trained at all. d) A type of machine learning where the algorithm is trained using only a portion of labeled data.

  4. What is unsupervised learning? a) A type of machine learning where the algorithm is trained using only unlabeled data to identify patterns and relationships in the data. - Answer b) A type of machine learning where the algorithm is trained using labeled data to make predictions on new, unseen data. c) A type of machine learning where the algorithm is not trained at all. d) A type of machine learning where the algorithm is trained using only a portion of labeled data.

  5. What is the difference between overfitting and underfitting in machine learning? a) Overfitting occurs when a model is too complex and fits the training data too closely, while underfitting occurs when a model is too simple and cannot capture the complexity of the data. - Answer b) Overfitting occurs when a model is too simple and cannot capture the complexity of the data, while underfitting occurs when a model is too complex and fits the training data too closely. c) Overfitting and underfitting are the same thing. d) Overfitting occurs when a model is too complex, while underfitting occurs when a model is too simple.

  6. What is the bias-variance tradeoff in machine learning? a) The bias-variance tradeoff refers to the balance between a model's ability to fit the training data well (low bias) and its ability to generalize to new, unseen data (low variance). - Answer b) The bias-variance tradeoff refers to the balance between a model's ability to fit the training data well (high bias) and its ability to generalize to new, unseen data (high variance). c) The bias-variance tradeoff refers to the balance between a model's ability to fit the training data poorly (high bias) and its ability to generalize to new, unseen data (low variance). d) The bias-variance tradeoff refers to the balance between a model's ability to fit the training data poorly (low bias) and its ability to generalize to new, unseen data (high variance).

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