Machine Learning MLE - Data Pre-processing & Feature Analysis

What is one of the methods used for data imputation?

Why is the curse of dimensionality a concern in machine learning?

What is a common strategy to avoid the curse of dimensionality?

Which of the following techniques can be used for dimensionality reduction?

What can be done if an algorithm panics when encountering missing data?

What is the primary goal of data normalization in machine learning?

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?

Which visualisation technique is best for exploring relationships between two continuous variables?

What does one-hot encoding primarily facilitate in machine learning?

Which of the following statements about data preprocessing is accurate?

What does Z-Normalisation rely on for its calculations?

What is the primary purpose of visualizing data before processing it?

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.

Explore the fundamental concepts of data pre-processing and feature analysis in machine learning. This quiz covers essential techniques such as data normalization, handling missing values, and the importance of feature representation. Test your understanding of data visualization methods and their roles in analyzing data patterns.