Questions and Answers
Which of the following are data preprocessing techniques? (Select all that apply)
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?
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Match the following imputation strategies with their descriptions:
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What is the shape of the transformed data when using DictVectorizer on 4 samples with 2 features?
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Data preprocessing involves several transformations applied to the raw data to make it more amenable for _____.
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What is the purpose of FeatureUnion?
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What is data preprocessing?
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Which of the following is a data cleaning technique?
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Which strategy is NOT used in data imputation?
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What does DictVectorizer do?
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How many features are present in the constructed feature matrix from the sample data?
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What is the purpose of feature extraction?
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Data imputation can only be used with numeric data.
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What is the default strategy used by SimpleImputer for missing value imputation?
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What library provides the SimpleImputer class?
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Which of the following datasets is used for data imputation in the example?
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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 featuresandTargetTransformedRegressor.
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 featuresandTargetTransformedRegressor.
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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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!
