COVID 19 Outcome Classifier
This is my analysis of different classifiers on WHO organization's dataset using scikit-learn.
View the notebook using nbviewer from here:
Best hyperparameters for each model
K-Nearest Neighbors (KNN)
Parameter |
value |
n_neighbors |
3 |
weights |
uniform |
algorithm |
auto |
leaf_size |
10 |
p |
2 |
metric |
minkowski |
metric_params |
None |
n_jobs |
None |
Parameter |
value |
penalty |
l2 |
dual |
False |
tol |
0.0001 |
C |
112.9 |
fit_intercept |
True |
intercept_scaling |
1 |
class_weight |
None |
random_state |
42 |
solver |
lbfgs |
max_iter |
100 |
multi_class |
auto |
verbose |
0 |
warm_start |
False |
n_jobs |
None |
l1_ratio |
None |
Parameter |
value |
priors |
None |
var_smoothing |
1000.0 |
Support Vector Machines (SVM)
--- |
Precision |
Recall |
f1 |
Cross Validation |
ROC |
AUC |
K-Nearest Neighbors (KNN) |
0.96 |
0.96 |
0.96 |
0.7069 |
--- |
0.90 |
Logistic Regression (LR) |
1.00 |
0.95 |
0.97 |
0.7055 |
--- |
0.93 |
Naïve Bayes (NB) |
1.00 |
0.86 |
0.92 |
0.9375 |
--- |
0.88 |
Decision Trees (DT) |
--- |
--- |
--- |
--- |
--- |
--- |
Support Vector Machines (SVM) |
--- |
--- |
--- |
--- |
--- |
--- |
- Understanding The Confusion Matrix From Scikit Learn