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 (D)</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. (C)</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 (B)</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 (C)</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 (C)</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 (A)</p> Signup and view all the answers

Which of the following best describes a false positive?

<p>Receiving a positive result incorrectly (B), Identifying a harmless item as malicious (C)</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 (D)</p> Signup and view all the answers

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

<p>Email spam detection (A)</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. (D)</p> Signup and view all the answers

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

<p>Both indicate incorrect results. (A), A false negative is a missed detection of a positive result. (B)</p> Signup and view all the answers

What is deducted in deductive machine learning?

<p>Specific conclusions from existing rules (B)</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. (C)</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 (D)</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 (A)</p> Signup and view all the answers

What is a characteristic feature of supervised learning?

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

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

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

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

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

What important characteristic defines a Perceptron?

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

Which application is NOT typically associated with pattern recognition?

<p>Financial Forecasting (D)</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. (C)</p> Signup and view all the answers

Which statement about Bayesian networks is true?

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

What are the two components of the Bayesian logic program?

<p>Logical and Quantitative (A)</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. (C)</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. (C)</p> Signup and view all the answers

What describes the vanishing gradients problem?

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

What is a primary function of unsupervised learning?

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

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

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

What is NOT a characteristic of machine learning?

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

What does reinforcement learning primarily involve?

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

What is the main goal of PAC Learning?

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

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

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

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

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

What does a hypothesis represent in machine learning?

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

Flashcards

Correlation

A relationship between two things, where one does not necessarily cause the other.

Overfitting

When a model learns to fit the training data too well, resulting in poor performance on new, unseen data.

Standard Deviation

A measure of how spread out the values are in a dataset. A low value means data points are close to the mean, high means they're spread out.

Variance

The square of standard deviation, measuring the variance or dispersion of data points from the mean.

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Multilayer Perceptron (MLP)

An artificial neural network with multiple layers of interconnected nodes, used for predicting outputs from inputs.

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Boltzmann Machine

A type of neural network used for optimization and learning, aiming to find the best solution to a problem.

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Data Bias

A type of error in machine learning where certain elements in the training data are given more weight than others.

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Supervised Learning

A type of machine learning where the model is trained on labeled data, to learn to predict an outcome.

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Confusion Matrix

A table used to analyze the performance of a supervised learning algorithm, showing true positives, false positives, true negatives, and false negatives.

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Bagging Algorithm

A technique used to reduce overfitting in models by creating multiple models from different random subsets of the data and averaging their predictions.

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Inductive Learning

The process of inferring patterns from specific observations to formulate general theories.

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Deductive Learning

The process of testing an existing theory by using specific observations to deduce conclusions.

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Time Series

A set of observations ordered chronologically, often used to model and predict future values.

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High Variance

A condition where a model performs well on the training data but poorly on unseen data, often due to overfitting.

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Model Accuracy

The ability of the model to make correct predictions on unseen data.

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Model Performance

A broader metric that encompasses various aspects of a model's effectiveness, such as accuracy, precision, recall, and F1-score.

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False Positive

A positive result from a test when the actual result should have been negative.

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False Negative

A negative result from a test when the actual result should have been positive.

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Semi-Supervised Learning

A type of machine learning that uses both labeled and unlabeled data during training.

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Testing Set

The process of evaluating a machine learning model's performance on unseen data.

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Classifier

An algorithm that automatically categorizes data into one or more groups, like 'Spam' or 'Not Spam' for emails.

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Genetic Algorithm

A search algorithm that uses a population of possible solutions to find the best one. Imagine a group of people trying different keys to open a door.

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Pattern Recognition

A field that involves identifying patterns in data, often using machine learning. Think of recognizing faces in a crowd.

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Perceptron

An algorithm used for supervised learning of binary classifiers. It involves learning from examples, like recognizing a dog in a picture.

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Isotonic Regression

A method used to fit ideal distances while preserving the relative order of dissimilarities. Imagine fitting lines to data points while keeping the relative spacing.

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Bayesian Network

A graphical model that represents probabilistic relationships between variables, like diseases and their symptoms. It can be used to predict the probability of different diseases given symptoms.

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Bayesian Logic Program

A probabilistic model that combines logical reasoning with probabilities. It uses both logical rules and numerical probabilities to predict the likelihood of events.

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Incremental Learning Algorithm

An algorithm that can learn from new data without needing to retrain on the entire dataset. Imagine a machine that can learn new words without having to re-learn all the words it knows.

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Vanishing Gradients Problem

The vanishing gradients problem occurs when a neural network doesn't effectively propagate gradient information back through its layers, especially in deeper networks. This hinders the training process, making it difficult for the model to learn from data.

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Methods for Overcoming Vanishing Gradients

Methods used to address the vanishing gradients problem in deep neural networks. They help ensure gradients are effectively propagated during training.

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Reinforcement Learning

A machine learning approach where the algorithm learns through trial and error, receiving rewards or penalties based on its actions. It attempts to maximize rewards by learning from its interactions with the environment.

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Learning to Learn (Meta-Learning)

A machine learning technique where the algorithm learns to learn, improving its ability to generalize to new tasks or environments.

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PAC Learning

A theoretical framework that analyzes how well a learning algorithm performs on new, unseen data based on its accuracy on training data and the complexity of the algorithm. It focuses on demonstrating that an algorithm can achieve low error rates with high probability.

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Principal Component Analysis (PCA)

A technique for transforming data into uncorrelated features by finding the directions of greatest variance. This helps in dimensionality reduction and simplifying data for analysis.

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Kernel Principal Component Analysis (KPCA)

A non-linear version of PCA that uses kernel functions to map data into a higher-dimensional space before performing PCA. This allows it to capture non-linear relationships in the data.

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Independent Component Analysis (ICA)

A method that identifies independent underlying sources from observed data. It finds linear combinations of the observed variables such that the resulting components are statistically independent.

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Hypothesis in Machine Learning

This is the process of finding the optimal way to map input data to output data, using the available information to learn a function that best represents the relationship between them. The model that approximates this function is called a hypothesis.

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Epoch in Machine Learning

The number of times a training set is presented to the learning algorithm during training. Each epoch represents one complete pass through the training data.

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Entropy in Machine Learning

A measure of the uncertainty or randomness in a dataset. It is related to the spread or variability of data points.

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Bias In Machine Learning

The difference between the expected output of a model and the actual output of the model. It is a measure of the model's inherent error, even if the model is trained perfectly on the data.

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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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