Which one is the best classification algorithm?
Don't forget this line:
'All models are wrong, but some models are useful.' - George Box
Here are 5 classification models to start with 🔽
Don't forget this line:
'All models are wrong, but some models are useful.' - George Box
Here are 5 classification models to start with 🔽
1. Logistic Regression
LR is mainly used for binary classifications, such as 'yes' or 'no' cases.
The output is between 0 and 1, so it can be translated into a probability.
It's effective with simple problems but may struggle with complex ones.
LR is mainly used for binary classifications, such as 'yes' or 'no' cases.
The output is between 0 and 1, so it can be translated into a probability.
It's effective with simple problems but may struggle with complex ones.
2. Decision Trees
Tree-based models split the data into different subsets based on the input.
It's easy to visualize and follow each step and see how the model works.
They are simple and effective, but be careful with overfitting!
Tree-based models split the data into different subsets based on the input.
It's easy to visualize and follow each step and see how the model works.
They are simple and effective, but be careful with overfitting!
3. Random Forest
Random Forest builds multiple decision trees to improve accuracy.
It's great for large datasets and reduces the risk of overfitting.
Each tree in the forest has a so-called vote, and the majority vote decides the outcome.
Random Forest builds multiple decision trees to improve accuracy.
It's great for large datasets and reduces the risk of overfitting.
Each tree in the forest has a so-called vote, and the majority vote decides the outcome.
4. Support Vector Machines (SVM)
SVM is effective for both linear and non-linear classification.
It works effectively when there is a clear margin between categories, but it also leaves some room for error.
It can be computationally expensive.
SVM is effective for both linear and non-linear classification.
It works effectively when there is a clear margin between categories, but it also leaves some room for error.
It can be computationally expensive.
5. K-Nearest Neighbors (KNN)
KNN classifies data based on the closest neighboring points.
It may be a struggle to find the optimal K value in the model.
Yet it's simple and effective with small datasets.
KNN classifies data based on the closest neighboring points.
It may be a struggle to find the optimal K value in the model.
Yet it's simple and effective with small datasets.
That's it for today.
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Thanks 😉
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