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Classification algorithms

Prentice

Hello,

So I've sort of finished my model. The last thing I now need to do is check which classification algorithm gives the best performances. I've made a selection of seven algorithms:

Naïve Bayes
SVM
k-NN
Neural Network
Logistic Regression
Decision Tree
Random Forest

I've tried them all on my model, but some don't work.

-So for the Logistic regression and SVM I understand that I need the operator Polynominal by Binominal Classification, this doesn't change any of my data right? It only changes to format to suit these algorithms?
-When I try naïve Bayes it suddenly gives me an error that my exampleset does not match my training set, but I don't get this error when I use k-NN, Decision Tree or Random Forest.
-Decision Tree gives me for all my examples exactly the same confidence which is very strange
-Lastly, Neural Network takes for some reason forever to load and I don't know why

Thanks for answering these questions
-Prentice

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Accepted answers

[Deleted User]

@Prentice
How many row do you have? do you use split data? if you use it divide your data 0.7 for train and 0.3 for test.
If you see any error you can see a yellow triangle in the operator you can use that and it will help you.
mbs

varunm1

Hello @Prentice

I see that you have a filter set to Maximum < 0.7, but naive Bayes the maximum attributes has 1 in all the examples. As the filter example is not satisfied it is not giving any attributes. I think this is the reason for the issue during the apply model operator. When I tested K-NN and SVM they have examples with a maximum attribute <0.7 and are able to produce results. I tried changing this filter value to <= 1.0 and the naive Bayes gave results without any errors. So my understanding is that in naive Bayes it is not satisfying your filter value.

Image: https://us.v-cdn.net/6030995/uploads/editor/qi/yqwkiwmvor5t.png

varunm1

Hello @Prentice

I am not sure why the confidences are either 0 or 1. But I think this is not wrong. I tried testing on titanic data set and can see predictions with high levels of confidence. @IngoRM might suggest some thing.

Image: https://us.v-cdn.net/6030995/uploads/editor/ox/g4e2umrho3rz.png

Question 2: It is not an automatic functionality as the process is continuous and the operators expect appropriate inputs if not the process fails. I think if they make it automatic it will be an issue in case if we do some mistake in the process which we cannot identify without process failing. If you really want this condition Maximum <0.7 the naive Bayes and decision tree results doesn't satisfy this criterion which means you can use other algorithms or change filter. These is my understanding.

Thanks
Varun

All comments

[Deleted User]

Prentice
Hi
For Logistic regression and SVM you can see the result of converting the data on your table.
.
For Decision Tree try to use information gain or gain ratio.
Good luck