This post is following of above post.
In this post, I will do some classification methods.
Firstly, I make binary variable.
I made a binary variable named high, that shows 1 when lpc_gdp is hgher than the mdeian and 0 when lower than median.
Before making models, I load caret package/
Then, I make LPM: Linear Probability Model, which is liear regression model with OLS estimate.
"atwm": Attitides Towards Working Mother is only significant variable.
Then, make a prediction and calculating how much prediction is correct.
Accuracy is 0.7941. This means 79.4% is correctly predicted.
Next, I use SVM: Support Vector Machine. I use e1071 package svm() function.
with SVM model, what is accuracy?
Accuracy is 0.8529. So SVM is betther than LPM.
Next, I use GAM: Generalized Additive Model. I use gam() function in mgcv package.
In GAM, s(atwm) and s(l_inf) are significant variables.
Let's plot GAM model.
The plot shows s(em) is not needed s().
So, let' make another GAM model, this time s(l_unem).
Let's make predictions with GAM model
Let's get accuracy og GAM.
Accuracy us 0.9118.
How about GAM2 model?
Accuracy is 0.8824. So, the fist GAM model is better.
Next, I use tree model.
I use tree() function in tree package. In this model, "atwm" is the most important variable and l_inf is next. Others are not important.
Let's get accuracy of tree model.
Accuracy is 0.8235.
Next, I use k-NN model.
I use knn3() function in caret package.
Let's get accuracy of knn.
Accuracy is 0.7353.
Then, let's compare those methods accuracy.
GAM model has the highest accuracy.
That's it. Thank you!
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