Okay, so in weaker classification, so let's say I train a few classification models. So let's say I train on a base and I create a star. And I train another model and see it iBk and Krysta. And I try another mother Mary Kay Stein Christa. Then I train another model LBI. Christa, I train another model, maybe rules I choose zero off za and Krista, I train JP and Krista.

So I have a few models here. So 90 basis 96% accuracy. Lazy iBk are on it by lazy k style isn't it for lazy lb lb isn't it tree lose row is a lotta tea tree decision tree is around it says. So, from these are all these are models I can try to select I say the models that has the highest accuracy. So I will say my base has the highest accuracy here then decision tree also has the highest accuracy here. So, based on these two models, I can select them for let's say deployment or for creating a software based on these two models.

So, talking about models, we can actually save the models and then these model can also be loaded into this Java application in this vehicle library. Then you can use this model to predict some of the data in your Java solve our Java application case. So, this is roughly how we use classification or how we train our models and then how we select models and then use them for this deployment or these are report writing or these are software development. Then we can also supply our own test set here. So, we can supply see some data here and then a lead, we call predict the test sale pre data variable in this test data here and then we will get a predicted result. So this is a Power punches