Machine Learning Interview Questions
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Questions and Answers

What does a confusion matrix primarily visualize in machine learning?

  • The performance of a classification algorithm (correct)
  • The dataset size
  • The correlation between features
  • The overall data distribution

    • Which approach is suggested for handling datasets suffering from high variance?

  • Use a single model for predictions
  • Eliminate all outliers
  • Implement the bagging algorithm (correct)
  • Increase the complexity of the model

    • Which of the following statements accurately describes inductive learning?

  • It always starts with a hypothesis.
  • It consists of four distinct stages.
  • It aims to test existing theories.
  • It moves from specific instances to generalizations. (correct)

    • What is one method for handling missing values in a dataset?

    <p>Use predictive models to estimate missing values</p> Signup and view all the answers

    In the context of machine learning, why is model accuracy considered crucial?

    <p>It defines the model's scoring performance.</p> Signup and view all the answers

    Which statement best describes a time series in machine learning?

    <p>Ordered data points with respect to time</p> Signup and view all the answers

    What is a critical step in the deductive learning process?

    <p>Formulating a hypothesis based on existing theory</p> Signup and view all the answers

    Which of the following is NOT a method for dealing with corrupted values in a dataset?

    <p>Creating a duplicate of the dataset</p> Signup and view all the answers

    What is the primary purpose of a training dataset in machine learning?

    <p>To build and refine the model</p> Signup and view all the answers

    Which of the following best describes a false positive?

    <p>Receiving a positive result incorrectly</p> Signup and view all the answers

    In the context of machine learning, what does semi-supervised learning utilize?

    <p>A small amount of labeled data and a large amount of unlabeled data</p> Signup and view all the answers

    What is a common application of supervised machine learning in business?

    <p>Email spam detection</p> Signup and view all the answers

    Which of the following statements about inductive machine learning is true?

    <p>It learns from a set of instances to draw conclusions.</p> Signup and view all the answers

    What is the difference between a false negative and a false positive?

    <p>Both indicate incorrect results.</p> Signup and view all the answers

    What is deducted in deductive machine learning?

    <p>Specific conclusions from existing rules</p> Signup and view all the answers

    Which of the following scenarios exemplifies a false negative?

    <p>A pregnancy test shows negative results while the user is pregnant.</p> Signup and view all the answers

    What is the primary function of a Multilayer Perceptron (MLP)?

    <p>To generate a set of outputs from given inputs</p> Signup and view all the answers

    Which type of error is described by overfitting in machine learning?

    <p>High accuracy on training data with low accuracy on new data</p> Signup and view all the answers

    What is a characteristic feature of supervised learning?

    <p>Labels are provided for training data</p> Signup and view all the answers

    What does a low standard deviation indicate about a dataset?

    <p>More values are clustered around the mean</p> Signup and view all the answers

    What is the purpose of a Boltzmann Machine in machine learning?

    <p>To optimize solutions to specified problems</p> Signup and view all the answers

    Which of the following correctly describes the difference between classification and regression?

    <p>Classification predicts discrete values; regression predicts continuous values</p> Signup and view all the answers

    What does variance refer to in the context of machine learning?

    <p>The spread of a dataset around its mean value</p> Signup and view all the answers

    Which of the following is NOT a type of machine learning?

    <p>Detached Learning</p> Signup and view all the answers

    Which of the following is NOT a type of classification algorithm?

    <p>Genetic Algorithm</p> Signup and view all the answers

    What important characteristic defines a Perceptron?

    <p>It is a binary classification algorithm.</p> Signup and view all the answers

    Which application is NOT typically associated with pattern recognition?

    <p>Financial Forecasting</p> Signup and view all the answers

    What is the primary purpose of using Isotonic Regression?

    <p>To ensure the predicted probabilities are well-balanced.</p> Signup and view all the answers

    Which statement about Bayesian networks is true?

    <p>They utilize a directed acyclic graph for representation.</p> Signup and view all the answers

    What are the two components of the Bayesian logic program?

    <p>Logical and Quantitative</p> Signup and view all the answers

    Which of the following statements is characteristic of Genetic Algorithms?

    <p>They act on a population of possible solutions.</p> Signup and view all the answers

    What is the function of the first component in a Bayesian logic program?

    <p>To capture the qualitative structure of the domain.</p> Signup and view all the answers

    What describes the vanishing gradients problem?

    <p>The network cannot propagate gradient information back to earlier layers.</p> Signup and view all the answers

    Which of the following is NOT a proposed method to overcome the vanishing gradient problem?

    <p>Support vector machines (SVMs)</p> Signup and view all the answers

    How does data mining differ from machine learning?

    <p>Data mining deals with large amounts of unstructured data.</p> Signup and view all the answers

    What is a primary function of unsupervised learning?

    <p>To find interesting directions in the data.</p> Signup and view all the answers

    Which algorithm technique is associated with self-learning from past data?

    <p>Reinforcement Learning</p> Signup and view all the answers

    What is NOT a characteristic of machine learning?

    <p>It requires constant human interference.</p> Signup and view all the answers

    Which of the following correctly defines a classifier in machine learning?

    <p>An algorithm that sorts data into categories based on features.</p> Signup and view all the answers

    What does reinforcement learning primarily involve?

    <p>Learning optimal actions through rewards and penalties.</p> Signup and view all the answers

    What is the main goal of PAC Learning?

    <p>To achieve low generalization error with high probability.</p> Signup and view all the answers

    Which technique is primarily focused on transforming data into uncorrelated features?

    <p>Principal Component Analysis (PCA)</p> Signup and view all the answers

    What are the three stages of building a model in machine learning?

    <p>Model Building, Model Testing, Applying the model</p> Signup and view all the answers

    Which application uses predictions based on the sequence of a customer’s previous purchases?

    <p>Product Recommendation</p> Signup and view all the answers

    What does a hypothesis represent in machine learning?

    <p>A model that approximates a target function.</p> Signup and view all the answers

    Which of the following is NOT a characteristic of Independent Component Analysis (ICA)?

    <p>Focuses on maximizing correlation among features.</p> Signup and view all the answers

    Which of the following statements best describes Kernel-based Principal Component Analysis (KPCA)?

    <p>It applies kernel methods for nonlinear transformation.</p> Signup and view all the answers

    What does the term 'epoch' refer to in machine learning?

    <p>An iteration of the learning algorithm on the entire training dataset.</p> Signup and view all the answers

    Study Notes

    Machine Learning Interview Questions and Answers

    • Machine learning is a subset of artificial intelligence, containing techniques that allow computers to analyze data and apply artificial intelligence applications.
    • Artificial intelligence (AI) is the development of computer systems able to perform tasks normally requiring human intelligence.
    • Deep learning is a type of machine learning algorithm using multiple layers to extract higher-level features from raw input.

    Difficulty of Machine Learning

    • Machine learning is complex, requiring more than six months of dedicated study (at 6-7 hours/day) for mastery.
    • Individuals with strong mathematical and analytical skills may master it in six months.

    Kernel Trick in SVM

    • A kernel trick is a method to classify non-linear data by projecting it into a higher-dimensional space where it can be linearly separated.

    Cross-Validation Techniques

    • Holdout Method: A portion of the training dataset is held out and used to evaluate the model trained on the remaining data.
    • K-Fold Cross-Validation: The data is divided into k subsets. In each iteration, one subset is used as the validation set, and the other k-1 subsets are used for training.
    • Stratified k-Fold Cross-Validation: This is used with imbalanced datasets, ensuring that the proportion of each class in the subsets mirrors the overall distribution.
    • Leave-P-Out Cross-Validation: In this method, p data points are left out for validation, while the remaining data is used for training.

    Bagging and Boosting Algorithms

    • Bagging: Merges the same type of predictions, decreasing variance.
    • Boosting: Merges different types of predictions, decreasing bias.

    Kernels in Support Vector Machines (SVM)

    • Kernels are mathematical functions that transform data for non-linear decision surfaces into linear equations in higher dimensions.
    • Common SVM kernel types include polynomial, Gaussian, radial basis function (RBF), Laplace RBF, hyperbolic tangent (sigmoid), Bessel function of the first kind and ANOVA radial basis kernel

    Out-of-Bag (OOB) Error

    • A technique for estimating the prediction error of random forests or boosted decision trees.
    • Uses a subsampling technique with replacement to create training samples.

    K-Means and K-NN Algorithms

    • K-Means: Unsupervised machine learning algorithm for clustering. Slower, and eager learner.
    • K-NN: Supervised machine learning algorithm for classification and regression. More accurate, and a lazy learner.

    Variance Inflation Factor (VIF)

    • VIF is a measure of multicollinearity in multiple regression variables.
    • A high VIF indicates that an independent variable is highly correlated with other variables.

    Support Vector Machines (SVM)

    • SVM is a supervised learning algorithm for both classification and regression problems.
    • Primarily used for classification, SVM creates an optimal decision boundary to classify data points in different categories.

    Supervised vs. Unsupervised Learning

    • Supervised Learning: The algorithm learns on labeled data, where input (X) is mapped to output (Y).
    • Unsupervised Learning: The algorithm learns on unlabeled data, to find patterns and structure.

    Precision and Recall

    • Precision/Positive Predictive Value: The fraction of relevant instances among the retrieved instances.
    • Recall/Sensitivity: The fraction of relevant instances that were retrieved.

    L1 and L2 Regularization

    • L1 Regularization (Lasso): Adds the absolute value of the magnitude of coefficients as a penalty term to the loss function. Estimates the median of data.
    • L2 Regularization (Ridge): Adds the squared magnitude of coefficients as a penalty term to the loss function. Estimates the mean of data.

    Fourier Transform

    • A mathematical tool to decompose a signal into its constituent sine wave components.
    • It is used in areas like signal processing, audio, and image analysis.

    F1 Score

    • A metric combining precision and recall to evaluate a classifier's performance, especially on imbalanced datasets.

    Type I and Type II Errors

    • Type I Error: False positive; rejecting a true null hypothesis.
    • Type II Error: False negative; failing to reject a false null hypothesis.

    ROC Curve

    • A graphical plot of true positive rate vs. false positive rate for a binary classification model, used to assess its performance.

    Different Machine Learning Algorithms

    • Decision trees, Naive Bayes, Random forests, Support vector machines, K-nearest neighbor, K-means clustering, Gaussian mixture model, Hidden Markov models, and more.

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    100 Machine Learning Interview Questions And Answers PDF

    Description

    This quiz covers key concepts in machine learning, including definitions, techniques, and important algorithms like the kernel trick and cross-validation methods. Prepare yourself for technical interviews with a focus on artificial intelligence and deep learning fundamentals.

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