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Applied Machine Learning ( End to End ) Classification Models
applied-machine-learning's Introduction
Model = Algorithm ( Parameters = Values ) + Data
- Data Type
- Information
- Rows and Columns
- Numerical or Categorical
- Find Missing Data
- Find Relation between Features and Labels
- Visual Data
- Fill or Drop Missing
- Encode Categorical Data
3. Split Datset into Train Set, Validate Set and Test Set
4. Fit | Train an Initial Model and Evaluate
- Use K Fold Cross Validation to get Better Accuracy and Observe the Cross Validation Score
5. Tune Hyperparameters by using Grid Search Cross Validation
- Apply Grid Search Cross Validation to Find Optimal Hyperparameters of a Model which results in the most Accurate Predictions
- Find Best Parameters
6. Evaluate on Validation Set
- Evaluate the Results on Validation Set using the Best Performing Parameters
- Create more than one Model to Find Best Performing Model for Test Set
7. Select and Evaluate the Final Model on Test Set
- Select the Final Best Performing Model on Test Set for Evaluation.
Model |
Type |
Train Speed |
Predict Speed |
Performance |
Logistic Regression |
Classification |
Fast |
Fast |
Low |
Support Vector Machine |
Classification |
Slow |
Moderate |
Medium |
Multi Layer Perceptron |
Both |
Slow |
Moderate |
High |
Random Forest |
Both |
Moderate |
Moderate |
Medium |
Boosted Tree |
Both |
Slow |
Fast |
High |
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