Data Pre-processing Techniques Quiz
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Questions and Answers

Which of the following are data preprocessing techniques? (Select all that apply)

  • Data Imputation (correct)
  • Data Compression
  • Data Cleaning (correct)
  • Feature Scaling (correct)

    • What does DictVectorizer do?

    Converts a list of dictionary objects to a feature matrix.

    Data imputation is required when there is no missing data in the dataset.

    False

    Which imputation strategy replaces missing values with the column's average?

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

    Match the following imputation strategies with their descriptions:

    <p>Mean = Replaces missing values with the column's average Median = Replaces missing values with the column's median Most Frequent = Replaces missing values with the most frequent value in the column Constant = Replaces missing values with a specified constant value</p> Signup and view all the answers

    What is the shape of the transformed data when using DictVectorizer on 4 samples with 2 features?

    <p>(4, 2)</p> Signup and view all the answers

    Data preprocessing involves several transformations applied to the raw data to make it more amenable for _____.

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

    What is the purpose of FeatureUnion?

    <p>To combine outputs from multiple transformations into a single transformed feature matrix.</p> Signup and view all the answers

    What is data preprocessing?

    <p>Data preprocessing involves several transformations applied to raw data to make it suitable for learning.</p> Signup and view all the answers

    Which of the following is a data cleaning technique?

    <p>Feature scaling</p> Signup and view all the answers

    Which strategy is NOT used in data imputation?

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

    What does DictVectorizer do?

    <p>DictVectorizer converts a list of dictionary objects to a feature matrix.</p> Signup and view all the answers

    How many features are present in the constructed feature matrix from the sample data?

    <p>2</p> Signup and view all the answers

    What is the purpose of feature extraction?

    <p>Feature extraction aims to derive a set of features from the initial dataset for model training.</p> Signup and view all the answers

    Data imputation can only be used with numeric data.

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

    What is the default strategy used by SimpleImputer for missing value imputation?

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

    What library provides the SimpleImputer class?

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

    Which of the following datasets is used for data imputation in the example?

    <p>Heart disease dataset</p> Signup and view all the answers

    Study Notes

    Data Pre-processing Techniques

    • Data preprocessing transforms raw data for improved model training and predictions.
    • Key stages include data cleaning, feature transformation, feature selection, and feature extraction.

    Data Cleaning Techniques

    • Data Imputation: Replaces missing values using strategies like mean, median, mode, or a specified constant.
    • Feature Scaling: Adjusts feature values to a common scale, improving algorithm performance.

    Feature Transformation

    • Polynomial Features: Generates new features based on polynomial combinations of existing ones.
    • Discretization: Converts continuous features into discrete categories.
    • Handling Categorical Features: Techniques for converting categorical variables into numerical formats for modeling.
    • Custom Transformers: User-defined transformations tailored for specific use cases.
    • Composite Transformers: Combines multiple transformations into a single operation; examples include Apply transformation to diverse features and TargetTransformedRegressor.

    Feature Selection

    • Filter-based Feature Selection: Uses statistical tests to select features based on their relevance.
    • Wrapper-based Feature Selection: Evaluates subsets of features based on model performance.

    Feature Extraction

    • PCA (Principal Component Analysis): Reduces dimensionality by transforming features into a lower-dimensional space.

    Utilization of Pipelines

    • Pipelines enable the specification of transformation order, ensuring consistent processing of data.
    • FeatureUnion: Combines outputs from multiple transformations to create a single feature matrix.

    Library Imports

    • Best practices recommend consolidating library imports in one cell, sorted alphabetically to avoid duplicates.
    • Common libraries include:
      • numpy
      • matplotlib
      • pandas
      • seaborn

    Feature Extraction with DictVectorizer

    • DictVectorizer: Transforms lists of dictionaries into a feature matrix suitable for machine learning models.
    • Sample data represents children's age and height, transformed into a matrix format.

    Data Imputation in Practice

    • Full feature matrices are essential for many machine learning algorithms; missing data can impede performance.
    • SimpleImputer: A tool from Sklearn for data imputation, handling various missing value strategies.
    • Important parameters include:
      • missing_values: Specifies the type of missing values (e.g., np.nan).
      • strategy: Options include 'mean', 'median', 'most_frequent', or 'constant' to determine how missing data is replaced.

    Heart Disease Dataset Example

    • The heart disease dataset comprises several features including:
      • Age
      • Sex (1 = male; 0 = female)
      • Chest pain type (cp)
      • Resting blood pressure (trestbps)
      • Serum cholesterol (chol)
      • Fasting blood sugar (fbs)
      • Resting electrocardiographic results (restecg)
      • Maximum heart rate achieved (thalach)
      • Exercise induced angina (exang)

    Data Pre-processing Techniques

    • Data preprocessing transforms raw data for improved model training and predictions.
    • Key stages include data cleaning, feature transformation, feature selection, and feature extraction.

    Data Cleaning Techniques

    • Data Imputation: Replaces missing values using strategies like mean, median, mode, or a specified constant.
    • Feature Scaling: Adjusts feature values to a common scale, improving algorithm performance.

    Feature Transformation

    • Polynomial Features: Generates new features based on polynomial combinations of existing ones.
    • Discretization: Converts continuous features into discrete categories.
    • Handling Categorical Features: Techniques for converting categorical variables into numerical formats for modeling.
    • Custom Transformers: User-defined transformations tailored for specific use cases.
    • Composite Transformers: Combines multiple transformations into a single operation; examples include Apply transformation to diverse features and TargetTransformedRegressor.

    Feature Selection

    • Filter-based Feature Selection: Uses statistical tests to select features based on their relevance.
    • Wrapper-based Feature Selection: Evaluates subsets of features based on model performance.

    Feature Extraction

    • PCA (Principal Component Analysis): Reduces dimensionality by transforming features into a lower-dimensional space.

    Utilization of Pipelines

    • Pipelines enable the specification of transformation order, ensuring consistent processing of data.
    • FeatureUnion: Combines outputs from multiple transformations to create a single feature matrix.

    Library Imports

    • Best practices recommend consolidating library imports in one cell, sorted alphabetically to avoid duplicates.
    • Common libraries include:
      • numpy
      • matplotlib
      • pandas
      • seaborn

    Feature Extraction with DictVectorizer

    • DictVectorizer: Transforms lists of dictionaries into a feature matrix suitable for machine learning models.
    • Sample data represents children's age and height, transformed into a matrix format.

    Data Imputation in Practice

    • Full feature matrices are essential for many machine learning algorithms; missing data can impede performance.
    • SimpleImputer: A tool from Sklearn for data imputation, handling various missing value strategies.
    • Important parameters include:
      • missing_values: Specifies the type of missing values (e.g., np.nan).
      • strategy: Options include 'mean', 'median', 'most_frequent', or 'constant' to determine how missing data is replaced.

    Heart Disease Dataset Example

    • The heart disease dataset comprises several features including:
      • Age
      • Sex (1 = male; 0 = female)
      • Chest pain type (cp)
      • Resting blood pressure (trestbps)
      • Serum cholesterol (chol)
      • Fasting blood sugar (fbs)
      • Resting electrocardiographic results (restecg)
      • Maximum heart rate achieved (thalach)
      • Exercise induced angina (exang)

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    Related Documents

    Week_2_MLP_Data_preprocessing - Colaboratory.pdf
    Week_2_MLP_Data_preprocessing - Colaboratory.pdf

    Description

    This quiz covers various data preprocessing techniques essential for improving the quality of raw data before model training. You'll explore methods like data cleaning, feature scaling, and transformation, including polynomial features and categorical handling. Test your knowledge and prepare for effective data handling!

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