Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
Tool Review: Lessons learned from using FeatureTools to simplify the process of Feature Engineering
Preparing Data for AI
Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
General Tips
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When you label individual images, it is better to use numerical labels (even though text labels are easier to understand) so that you can avoid mapping between numbers (use …
Loss Functions for Machine Learning Models
Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
Tips and Traps
- A Loss function is always non-negative. If you get a negative loss when training a model, there must be something wrong with the code. For example, maybe you …
Entropy
Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
Entropy and Related Concepts
- Entropy
- Shannon Entropy
- Cross Entropy
- K-L divergence
Tips
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The entropy concept was first introduced for discrete distributions (called Shannon entropy), which is defined as
$$H(X) = E …
Rule-base Image Process
Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
If you face a relative simple image recognition problem which hasn't been studied by other people before so that no public data is available for it, it is probably less effort …
Handle Categorical Variables in LightGBM
Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
LightGBM support pandas columns of category type.
As a matter of fact,
this is the suggested way of handling categorical columns in LightGBM.
data[feature] = pd.Series(data[feature], dtype="category")
A LightGBM model (which is a Booster object) records categories of each categorical feature. This information is used to set categories of each categorical feature during prediction, which ensures that a LightGBM model can always handle categorical features correctly.