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So I want to use these three data, the sensor’s distance from the ground ( since the distance will be different for a ground with ice and non-ice ), humidity, and surface temp.
I wanted to use SVM method because I thought the data was clean enough and using neural network will be too much.
Please help me out.. I just started learning machine learning..
So, I want to do the multiclass classification with predicting “Dry Ice Environment ( Created O )”,”Dry Ice Environment ( Created X )”,”Wet Road”,”Dry Road”..
And used three features the sensor’s distance from the ground, humidity, and surface temp.
- How could I make a good predicting supervised machine learning algorithm?
- I tried SVM and NN method, and does two got accuracy of 1.0 but printed out the different result, and through searching I found out that it might be overfitting. Is it? and how to fix?
- I find it weird that precision recall f1-score and the accuracy has metrics of 1.0.
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