Isolation Forest: A Tree based approach for Outlier Detection (Clearly Explained)
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- Опубліковано 8 лип 2024
- Welcome to the fifteenth video of the series "Build your First Machine Learning Project". In this, we'll see Isolation Forest Algorithm for outlier detection.
Isolation Forest is a simple yet incredible algorithm that is able to spot outliers or anomalies in the data. Let's understand how the Isolation forest algorithm for Outlier detection works.
Chapters
0:00 Intro to Isolation Forest
2:10 How does Isolation forest algorithm work?
12:50 Implementing in Python
17:45 Conclusion
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Previous Lesson:
Why mahalanobis distance is incredibly powerful for outlier detection : • Why mahalanobis distan...
Earlier Lessons:
1. Build your first ML Project: • Build Your FIRST Machi...
2. How to Formulate ML Problem: • Build Your First ML Pr...
3. Setup Python Environment: • Setup Python Environme...
4. Jupyter Notebook Tutorial: • Jupyter Notebook Tutor...
5. What is ML Modeling: • What is ML Modeling? (...
6. Reduce the size of Pandas Dataframe: • Reduce the memory size...
7. What is EDA: • Exploratory Data Analy...
8. How to impute missing Data: • How to handle missing ...
9. Mice Imputation Algorithm: • Multiple Imputation by...
10. How to impute missing data in categorical Variables: • How to impute missing ...
11. How to Detect Outliers with Z Score: • How to Detect Outliers...
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One of the best explanations out here! Thankyou
helped me a lot, keep up he good work!
Nice ...Thanks
Hi, my dataset consists of categorical values and I’ve label encoded them to use isolation forest model. But how to evaluate my model? What metrics should I follow?
If your 'features' are categorical, don't label encode then. Label encoding is meant for Target variables.
Evaluating models can be done as you would with any other predictive model
@@machinelearningplus but I don’t have a target variable and all the data is categorical how do you think I can proceed?? Btw thanks for your reply
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