IrisClassification
Classification using iris dataset
Data Set
Iris dataset is perhaps the best known database to be found in the pattern recognition. It has 3 classes that are Iris-setosa, Iris-verticolor, Iris-virginica. The data set contains 3 classes of 50 instances each.
Procedure
To know the data well we tried to built different kinds of graphs. After that perform training and testing over iris data collectively. There we get to know about accuracies over different models Then we will use 1 Petal Feature and 1 Sepal Feature to check the accuracy of the algorithm as we are using only 2 features that are not correlated. Thus we can have a variance in the dataset which may help in better accuracy.
Accuracy Table
Overall accuracy of dataset over different model
| Algo | SVM | DecisionTree | KNN | LogisticR |
|---|---|---|---|---|
| acc | 0.98095 | 0.97142 | 0.97142 | 0.96190 |
Accuracy of Petal and Sepal seperatly
| Algo | SVM | DecisionTree | KNN | LogisticR |
|---|---|---|---|---|
| Petal Acc. | 0.97 | 0.95 | 0.97 | 0.68 |
| Sepal Acc. | 0.8 | 0.42 | 0.75 | 0.64 |