Encoding and Data Standardization Quiz
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Questions and Answers

What type of data represents specific categories or groups?

  • Categorical data (correct)
  • Continuous data
  • Ordinal data
  • Numerical data
  • How is categorical data typically represented in machine learning models?

  • Using the 'int' data type
  • Using the 'float' data type
  • As a separate category
  • Using the 'object' or 'string' data type (correct)
  • Which type of encoding is suitable for categorical data with no natural order or ranking between categories?

  • Binary Encoding
  • Frequency Encoding
  • Ordinal Encoding
  • One-Hot Encoding (correct)
  • Ordinal variables represent categories that have:

    <p>A natural order or ranking</p> Signup and view all the answers

    What is the purpose of Ordinal Encoding?

    <p>Converting categorical variables to numerical values based on ranking</p> Signup and view all the answers

    Which approach converts each category of a categorical variable into a separate binary column?

    <p>One-Hot Encoding</p> Signup and view all the answers

    What is the main purpose of standardization in data preprocessing?

    <p>Transforming numeric features to have a mean of 0 and a standard deviation of 1</p> Signup and view all the answers

    Which technique involves scaling numeric features to a fixed range?

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

    What is the role of data encoding in machine learning workflows?

    <p>Transforming categorical data into numerical form for model compatibility</p> Signup and view all the answers

    Which method is suitable for converting categorical variables into binary vectors?

    <p>One-Hot Encoding</p> Signup and view all the answers

    How does standardization contribute to better decision-making in machine learning?

    <p>By transforming numeric features to have a mean of 0 and a standard deviation of 1</p> Signup and view all the answers

    What is the purpose of Ordinal Encoding?

    <p>To assign integer values to unique category values</p> Signup and view all the answers

    When is One-Hot Encoding typically used?

    <p>When no ordinal relationship exists between categories</p> Signup and view all the answers

    What problem does data standardization aim to solve?

    <p>Handling large differences between ranges of input data features</p> Signup and view all the answers

    Which method is commonly used for standardizing data?

    <p>Z-score</p> Signup and view all the answers

    What does a low standard deviation indicate in a dataset?

    <p>Data points are close to the mean</p> Signup and view all the answers

    In which scenario does One-Hot Encoding perform better than Ordinal Encoding?

    <p>When no natural ordering should be assumed between categories</p> Signup and view all the answers

    Study Notes

    Data Preprocessing

    • Ordinal Encoding: assigns an integer value to each unique category value (e.g., "Excellent" = 1, "Good" = 2, "Bad" = 3)
    • One-Hot Encoding: used for categorical variables with no ordinal relationship, prevents model from assuming a natural ordering between categories

    Data Standardization

    • Necessary when features have large differences in ranges or are measured in different units
    • Prevents features with larger values from dominating distance computations
    • Z-score is a popular method for standardizing data, which transforms features to comparable scales
    • Z-score formula: (X - μ) / σ, where μ is the mean and σ is the standard deviation

    Data Standardization: Mean and Standard Deviation

    • Mean (μ): a measure of central tendency
    • Standard Deviation (σ): a measure of the dispersion or spread of a dataset around its mean
    • Low standard deviation: data points tend to be close to the mean
    • High standard deviation: data points are spread out over a wider range of values

    Data Quality Issues

    • Missing values
    • Duplicate data
    • Data imbalance
    • Data bias

    Categorical Data

    • Represents specific categories or groups
    • Non-numerical and consists of labels or qualitative values
    • Examples: Gender, Marital Status, Occupation
    • Cannot be directly processed by machine learning models

    Encoding for Categorical Data

    • Nominal variables: categories without any specific order or ranking
    • Ordinal variables: categories with a natural order or ranking
    • Common approaches: Ordinal Encoding and One-Hot Encoding

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    Description

    Test your knowledge on encoding techniques like Ordinal and One hot Encoding, as well as data standardization in machine learning. Learn when to use each technique and their implications on model performance.

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