Neural networks model for classification
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Hi
i am new to neural networks and just used it for classification on a dataset. As i read that neural networks are particularly well suited for complex decision boundary problems over many variables, i used it for my problem.
my problem can be defined as 40 variables which affect the output class
the dataset has 40 input variables: for eg: 1: air temperature 2: humidity value 3: vegetation density in % 4. radiation values and similar variables which affect the final output class "1 or 0"
1 : indicating heavy rainfall 0 : less rainfall
airtemperature humidityvalue vegetationdensity% radiationvalue Class
32 54 80 32 1
34 76 90 54 1
12 23 21 11 0
22 74 10 82 0
So i have run neural networks on a dataset and it gave me around 91% accuracy over the trainig set and 86% accuracy over the testing set.
Can I directly use this neural network model to test on another dataset?
To explain more my question is : If i have similar another data set for a different region, will I be able to predict how the results will vary?
Thanks!
4 件のコメント
Greg Heath
2015 年 12 月 3 日
Your question is clear. However, it cannot be answered without a quantitative comparison of the two sets of data.
It might be helpful to try to reduce the dimensionality of the input. I would try PLSREGRESS before STEPWISE, STEPWISEFIT or SEQUENTIALFS.
Hope this helps,
Greg
Greg Heath
2015 年 12 月 4 日
SORRY. I meant to say
I would try STEPWISE, STEPWISEFIT or SEQUENTIALFS before PLSREGRESS
Hope this helps.
Greg
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