Why mahalanobis distance is incredibly powerful for outlier detection
Welcome to the thirteenth video of the series "Build your First Machine Learning Project". In this, we'll see how to detect Multi variate outliers with Mahalanobis Distance.
Notebook link: https://github.com/machinelearningplu...
The Mahalanobis distance is one of the most powerful distance measures in multivariate statistics.
It can be used to determine whether a sample is an outlier, whether a process is in control or whether a sample is a member of a group or not.
So let's understand it.
Chapters
0:00 Intro
2:42 What is Mahalanobis Distance
4:22 Difference between Euclidean and Mahalanobis Distance
8:02 Formula behind Mahalanobis Distance
12:11 Code behind Mahalanobis Distance
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Previous Lesson:
How to Detect Outliers with Z Score : • How to Detect Outliers with Z Score |...
Earlier Lessons:
1. Build your first ML Project: • Build Your FIRST Machine Learning Pro...
2. How to Formulate ML Problem: • Build Your First ML Project part 2: ...
3. Setup Python Environment: • Setup Python Environment using ANACONDA
4. Jupyter Notebook Tutorial: • Jupyter Notebook Tutorial - How to In...
5. What is ML Modeling: • What is ML Modeling? (Problem stateme...
6. Reduce the size of Pandas Dataframe: • Reduce the memory size of Pandas Data...
7. What is EDA: • Exploratory Data Analysis (EDA) - Use...
8. How to impute missing Data: • How to handle missing data for machin...
9. Mice Imputation Algorithm: • Multiple Imputation by Chained Equati...
10. How to impute missing data in categorical Variables: • How to impute missing data in categor...
11. Detect Outliers with IQR and Boxplot?: • How to Detect Outliers with IQR and B...
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