Introduction to Machine Learning - Workflow
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Questions and Answers

What should be defined first when dealing with a machine learning problem?

  • The problem type
  • The data collection methods
  • The input data and expected outputs (correct)
  • The measure of success
  • Why is it important to collect better data for a machine learning model?

  • It increases the computational cost
  • It helps improve the model's performance (correct)
  • It reduces the complexity of the model
  • It simplifies the data preprocessing steps
  • Which of the following is NOT considered a measure of success in machine learning?

  • Cost optimization (correct)
  • Accuracy
  • Customer-retention rate
  • Precision
  • What key assumption is made when using machine learning models?

    <p>The future will behave like the past</p> Signup and view all the answers

    In the context of machine learning, what does the last column in the Boston housing dataset represent?

    <p>Target variable (median house price)</p> Signup and view all the answers

    Study Notes

    Introduction to Machine Learning - Workflow

    • Machine learning problems require a specific methodology
    • Define the Problem: Crucial first step. Determine inputs, outputs, and the objective.
      • What is the main objective?
      • What is the input data? Is it available?
      • What type of problem (e.g., binary classification, clustering)?
      • What is the expected output?
    • Collect Data: Essential for model development. The more and better data, the better the model performs. Data typically has a specific shape.
    • Choose a Measure of Success: Define how success will be measured; e.g., precision, accuracy, customer retention, mean squared error (MSE) for regression, or precision, accuracy, and recall for classification.

    Evaluation Protocol

    • Hold-out Validation Set: Set aside a portion of the data as a test set. Train on the remaining data, tune parameters using the validation set, and finally evaluate on the test set.
    • K-Fold Validation: Divide data into K partitions. Train on K-1 partitions and evaluate on the remaining partition. Repeat for each partition. The final score is the average of the K scores.
    • Iterated K-Fold Validation with Shuffling: Apply K-fold validation multiple times with data shuffled between runs. Helps to make sure that the model generalizes well
    • Data Representation: Data should accurately represent the problem. Avoid redundancies and temporal leaks.
    • Avoid Duplicates: Remove duplicate data points to avoid inaccurate learning by the model.

    Data Preparation

    • Missing Data: Common problem in real-world data. Methods to handle:
      • Removing samples or features with missing values.
      • Imputing missing values (e.g., using the mean).
    • Categorical Data: Ordinal or nominal.
      • Ordinal: Features that can be sorted (e.g., size).
      • Nominal: Features without inherent order (e.g., color).
    • Feature Scaling: Important for many algorithms.
      • Normalization: Rescales features to a range of [0, 1].
      • Standardization: Centers features at mean 0 with standard deviation 1.
    • Selecting Meaningful Features: Identify and remove redundant features to avoid overfitting. This can be done using methods like Principal Component Analysis (PCA).
    • Splitting Data: Split data into subsets like training, testing, and validation sets. Testing set is used to evaluate overall performance, while validation helps tune the model's parameters.

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    Description

    This quiz explores the essential workflow of machine learning, covering steps like defining the problem, data collection, and evaluation protocols. It provides insights into methodologies such as hold-out and K-fold validation. Test your understanding of these critical concepts in machine learning!

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