Machine Learning Midterm
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Questions and Answers

What is the primary goal of Supervised Learning?

  • Segmenting customers into groups
  • Ensuring the model generalizes well to unseen data
  • Predicting output from labeled input data (correct)
  • Evaluating the performance of a classification algorithm
  • What is the purpose of Cross-Validation in Machine Learning?

  • To segment customers into groups
  • To ensure the model generalizes well to unseen data (correct)
  • To evaluate the performance of a classification algorithm
  • To predict output from labeled input data
  • What is an example of a Feature Selection Method?

  • Confusion Matrix
  • Principal Component Analysis (correct)
  • Linear Regression
  • K-Means Clustering
  • What is the role of an Activation Function in Neural Networks?

    <p>To introduce non-linearity</p> Signup and view all the answers

    What is a Hyperparameter in Machine Learning?

    <p>A parameter that is set before the learning process begins</p> Signup and view all the answers

    What is the primary purpose of the Backpropagation algorithm in Machine Learning?

    <p>To update the weights by minimizing the loss function</p> Signup and view all the answers

    Which of the following metrics is most appropriate for evaluating imbalanced classification problems?

    <p>Precision-Recall Curve</p> Signup and view all the answers

    What is the primary goal of Principal Component Analysis (PCA) in Machine Learning?

    <p>To transform data into a lower-dimensional space</p> Signup and view all the answers

    What is the primary purpose of Dropout in Neural Networks?

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

    What is the primary technique used to handle missing data in Machine Learning?

    <p>Imputation</p> Signup and view all the answers

    Study Notes

    Types of Machine Learning

    • Supervised Learning uses labeled data to train models, aiming to predict output from labeled input data.
    • Primary goal of Supervised Learning is to predict output from labeled input data.

    Classification Algorithms

    • Logistic Regression is a common algorithm used in Classification.

    Clustering Algorithms

    • K-Means Clustering is used to segment customers into groups.

    Model Evaluation

    • Cross-Validation ensures the model generalizes well to unseen data.

    Neural Networks

    • An Epoch is a single forward and backward pass of all the training examples.

    Classification Model Metrics

    • Mean Squared Error is not a metric used in Classification models.

    Overfitting

    • Overfitting occurs when a model performs well on training data but poorly on test data.

    Activation Functions

    • Activation Functions introduce non-linearity into the model.

    Feature Selection Methods

    • Principal Component Analysis (PCA) is an example of a Feature Selection Method.

    Regression Algorithms

    • Linear Regression is a common algorithm used in Regression.

    Performance Evaluation

    • Confusion Matrix is used to evaluate the performance of a classification algorithm.

    Sequential Decision Making

    • Reinforcement Learning is used for Sequential Decision Making.

    Data Preprocessing

    • Normalization is used to scale data to a standard range.

    Deep Learning Models

    • Deep Learning Models automatically learn feature representations.
    • Convolutional Neural Networks (CNNs) are commonly used for Image recognition.

    Hyperparameters

    • Hyperparameters are set before the learning process begins.

    Ensemble Learning

    • Ensemble Learning uses multiple models to achieve better performance.

    Unsupervised Learning

    • K-Means Clustering is an example of Unsupervised Learning.

    Curse of Dimensionality

    • Curse of Dimensionality refers to the problem of overfitting in high-dimensional spaces.

    Reinforcement Learning

    • Policy is the strategy that the agent employs to determine its actions.

    Imbalanced Classification

    • Precision-Recall Curve is an appropriate metric for Imbalanced Classification.

    Backpropagation Algorithm

    • Backpropagation Algorithm updates the weights by minimizing the loss function.

    Classification Errors

    • False Positive occurs when a negative class is incorrectly identified as positive.

    Handling Missing Data

    • Imputation is a technique used to handle missing data.

    k-Nearest Neighbors (k-NN) Algorithm

    • k-NN Algorithm uses a distance metric to classify new points.

    Bootstrapping

    • Bootstrapping is a resampling technique used to estimate statistics on a population.

    Preventing Overfitting

    • Regularization techniques are used to prevent overfitting.

    Dropout

    • Dropout is a regularization method used to prevent overfitting in Neural Networks.

    Principal Component Analysis (PCA)

    • PCA transforms data into a lower-dimensional space.

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    Related Documents

    ML-MIDTERM-HANDOUT.pdf

    Description

    This quiz covers the basics of machine learning, including types of learning, common algorithms, and model evaluation techniques. Test your understanding of supervised learning, classification algorithms, and more.

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