Podcast
Questions and Answers
What is one of the methods used for data imputation?
What is one of the methods used for data imputation?
- Using mean or median values (correct)
- Excluding all features with missing data
- Generating synthetic data
- Random sampling from dataset
Why is the curse of dimensionality a concern in machine learning?
Why is the curse of dimensionality a concern in machine learning?
- Data samples become too sparse in the feature space. (correct)
- It simplifies the model training process.
- It makes algorithms run faster.
- It leads to a loss of important features.
What is a common strategy to avoid the curse of dimensionality?
What is a common strategy to avoid the curse of dimensionality?
- Increase the number of data samples (correct)
- Use only one feature for analysis
- Increase the number of irrelevant features
- Ignore dimensionality issues completely
Which of the following techniques can be used for dimensionality reduction?
Which of the following techniques can be used for dimensionality reduction?
What can be done if an algorithm panics when encountering missing data?
What can be done if an algorithm panics when encountering missing data?
What is the primary goal of data normalization in machine learning?
What is the primary goal of data normalization in machine learning?
Which of the following is NOT a method of data normalization?
Which of the following is NOT a method of data normalization?
What might be a consequence of not normalizing feature values before training a machine learning model?
What might be a consequence of not normalizing feature values before training a machine learning model?
Which visualisation technique is best for exploring relationships between two continuous variables?
Which visualisation technique is best for exploring relationships between two continuous variables?
What does one-hot encoding primarily facilitate in machine learning?
What does one-hot encoding primarily facilitate in machine learning?
Which of the following statements about data preprocessing is accurate?
Which of the following statements about data preprocessing is accurate?
What does Z-Normalisation rely on for its calculations?
What does Z-Normalisation rely on for its calculations?
What is the primary purpose of visualizing data before processing it?
What is the primary purpose of visualizing data before processing it?
Flashcards
Data Imputation: Removing instances with missing features
Data Imputation: Removing instances with missing features
Removing data points with missing features. This is often a safer approach when you have a large dataset.
Data Imputation: Mean/Median/Mode Imputation
Data Imputation: Mean/Median/Mode Imputation
Replacing missing values with the average or most frequent values.
Data Imputation: Machine Learning-Based Imputation
Data Imputation: Machine Learning-Based Imputation
Using a machine learning model to estimate missing values based on other features.
Curse of Dimensionality
Curse of Dimensionality
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Feature Selection and Dimensionality Reduction
Feature Selection and Dimensionality Reduction
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Data Preprocessing
Data Preprocessing
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Feature Representation
Feature Representation
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Boxplot
Boxplot
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Histogram
Histogram
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Scatter Plot
Scatter Plot
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One-Hot Encoding
One-Hot Encoding
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Z-Normalisation
Z-Normalisation
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Min-Max Normalisation
Min-Max Normalisation
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Study Notes
Machine Learning (MLE) - Data Pre-processing & Feature Analysis
- Machine learning processes data using a pipeline including data representation, modeling, evaluation, and optimization.
- Data understanding involves grasping the underlying problem and visualizing data characteristics like outliers and value ranges.
- Feature representation focuses on reliability and categorizing features as categorical, binary, or continuous.
- Feature value normalization ensures features are appropriately scaled.
- Preprocessing addresses missing data and errors using strategies like data imputation.
- Data visualization techniques like boxplots, histograms, and scatter plots help understand and analyze data patterns.
- Categorical data can be converted using one-hot encoding.
- Data normalization methods include Z-normalization (zero-mean normalization), min-max normalization, and vector normalization.
- Z-normalization calculates deviations from the mean and standard deviation.
- Min-max normalization scales data within a specific range.
- Vector normalization scales data to unit length.
- Advantages of data normalization include maintaining original data distribution, improved model numerical stability, and lessened impact on distance-based algorithms.
- Data imputation methods fill in missing data using approaches like using mean/median values, frequent values, k-nearest neighbors, multivariate imputation, and machine learning models.
- Curse of dimensionality occurs when the number of data instances is insufficient compared to the number of features, leading to sparse data and reduced model effectiveness.
- To mitigate the curse of dimensionality, increasing the number of data samples or reducing the number of features is crucial
- Feature selection and dimensionality reduction techniques are employed to achieve this.
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