simple-feed | gem implements a flexible time | Application Framework library

Today I was working on a classifier to detect whether or not a mushroom was poisonous given its features. The data was in a .csv file(read to a pandas DataFrame) and the link to the data can be found at the end.

I used sci-kit learn's train_test_split function to split the data into training and testing sets.

I then removed the column that specified whether or not the mushroom was poisonous or not for the training and testing labels and assigned this to a yTrain, and yTest variable.

I then applied a one-hot-encoding (Using pd.get_dummies()) to the data since the parameters were categorical.

After this, I normalized the training and testing input data.

Essentially the training and testing input data was a distinct list of one-hot-encoded parameters and the output data was a list of one's and zeroes representing the output(one meant poisonous, zero meant edible).

I used Keras and a simple-feed forward network for this project. This network is comprised of three layers; A simple Dense(Linear Layer for PyTorch users) layer with 300 neurons, a Dense layer with 100 neurons, and a Dense layer with two neurons, each representing the probability of whether or not the given parameters of the mushroom signified it was poisonous, or edible. Adam was the optimizer that I had used, and Sparse-Categorical-Crossentropy was my loss-function.

I trained my network for 60 epochs. After about 5 epochs the loss was basically zero, and my accuracy was 1. After training, I was worried that my network had overfitted, so I tried it on my distinct testing data. The results were the same as the training and validation data; the accuracy was at 100% and my loss was negligible.

My validation loss at the end of 50 epochs is 2.258996e-07, and my training loss is 1.998715e-07. My testing loss was 4.732502e-09. I am really confused at the state of this, is the loss supposed to be this low? I don't think I am overfitting, and my validation loss is only a bit higher than my training loss, so I don't think that I am underfitting, as well.

Do any of you know the answer to this question? I am sorry if I had messed up in a silly way of some sort.

Link to dataset: https://www.kaggle.com/uciml/mushroom-classification