Rain Classification in Australia
Under this context, sklearn classification algorithms will be used, namely:
Logistic Regression Classification (Parametric) Decision Tree Classification (Non parametric) Random Forest Classification (Non parametric) K-Nearest Neighbour (KNN) Classification (Non parametric)
Train Test Split is 80-20
Accuracy Score :
Logistic Regression Test Score 0.854062
Logistic Regression Train Score 0.853797
Decision Tree Test Score 0.795838
Decision Tree Train Score 1.000000
Random Forest Test Score 0.858269
Random Forest Train Score 0.999978
K-Nearest Neighbour Test Score 0.817180
K-Nearest Neighbour Train Score 0.831138
`Null accuracy score: 0.7815`
Logistic Regression is performing good without overfitting
.
But if look at accuracy ,Random Forest accuracy is better.
How to check for underfitting
.
Confusion Matrix
ROC AUC :
ROC AUC For LR : 0.8742
ROC AUC For DT : 0.7072
ROC AUC For RF : 0.8883
ROC AUC For KNN : 0.7928
Classification Metrices
Classification accuracy : 0.8583
Classification error : 0.1417
Precision : 0.9586
Recall : 0.8726
True Positive Rate : 0.8726
False Positive Rate : 0.2286
Specificity : 0.7714
Logistic Regression
precision recall f1-score support
No 0.88 0.95 0.91 17650
Yes 0.74 0.52 0.61 4935
accuracy 0.85 22585
macro avg 0.81 0.73 0.76 22585
weighted avg 0.85 0.85 0.84 22585
Decision Tree
precision recall f1-score support
No 0.87 0.86 0.87 17650
Yes 0.53 0.55 0.54 4935
accuracy 0.80 22585
macro avg 0.70 0.71 0.70 22585
weighted avg 0.80 0.80 0.80 22585
Random Forest Classification
precision recall f1-score support
No 0.87 0.96 0.91 17650
Yes 0.77 0.50 0.61 4935
accuracy 0.86 22585
macro avg 0.82 0.73 0.76 22585
weighted avg 0.85 0.86 0.85 22585
K-Nearest Neighbour (KNN) Classification
precision recall f1-score support
No 0.84 0.95 0.89 17650
Yes 0.66 0.34 0.45 4935
accuracy 0.82 22585
macro avg 0.75 0.64 0.67 22585
weighted avg 0.80 0.82 0.79 22585
Evaluation should be done on Logistic only or on all four .