🤖 Baseline ML Models

[CSFelix]

Usefull links

• Intro to Keras with breast cancer data[ANN]

• Intro to Recurrent Neural Networks LSTM | GRU

[CSFelix]

Now, even though RNNs are quite powerful, they suffer from Vanishing Gradient Problem which hinders them from using long term information, like they are good for storing memory 3-4 instances of past iterations but larger number of instances don't provide good results so we don't just use regular RNNs. Instead, we use a better variation of RNNs: Long Short Term Networks (LSTM).

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Components of LSTMs

• Forget Gate “f” ( a neural network with sigmoid);

• Candidate layer “C"(a NN with Tanh);

• Input Gate “I” ( a NN with sigmoid );

• Output Gate “O”( a NN with sigmoid);

• Hidden state “H” ( a vector );

• Memory state “C” ( a vector);

• Inputs to the LSTM cell at any step are Xt (current input) , Ht-1 (previous hidden state ) and Ct-1 (previous memory state);

• Outputs from the LSTM cell are Ht (current hidden state ) and Ct (current memory state);

[CSFelix]

There's no need to use LSTM and GRU since the dataset is not a Time Series at all.

[CSFelix]

Models

• [x] Linear Regression
• [x] Random Forest Regressor
• [x] XGBoost