Sign up to save your podcasts
Or
Share Unit 2 | Ep 03: Outliers – Noise or Signal?
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
Unit 2 | Ep 03: Outliers – Noise or Signal?
Welcome to Mindforge ML. In this episode, we investigate the rebels of your dataset: outliers.
An outlier can be a critical insight (fraud detection) or a disastrous error (sensor glitch). The difference lies in context. We move beyond simple deletion to explore detection and sophisticated treatment strategies.
Key topics:
Detection: Using Z-scores, IQR, and Isolation Forests to hunt down anomalies.
The Choice: Deciding when to remove, cap, or keep extreme values.
Visualization: Spotting problems with box plots and scatter plots.
Context: Why domain knowledge is your best tool for outlier management.
Stop blindly deleting data. Learn to read the extremes.
Series: Mindforge ML | Unit 2Produced by: Chatake Innoworks Pvt. Ltd.Initiative: MindforgeAI
View all episodes
By CI CodesmithWelcome to Mindforge ML. In this episode, we investigate the rebels of your dataset: outliers.
An outlier can be a critical insight (fraud detection) or a disastrous error (sensor glitch). The difference lies in context. We move beyond simple deletion to explore detection and sophisticated treatment strategies.
Key topics:
Detection: Using Z-scores, IQR, and Isolation Forests to hunt down anomalies.
The Choice: Deciding when to remove, cap, or keep extreme values.
Visualization: Spotting problems with box plots and scatter plots.
Context: Why domain knowledge is your best tool for outlier management.
Stop blindly deleting data. Learn to read the extremes.
Series: Mindforge ML | Unit 2Produced by: Chatake Innoworks Pvt. Ltd.Initiative: MindforgeAI