on 10-Jan-2023 (Tue)

When an analyst uses feature engineering to predict behavior, the performance of the model will depend greatly on the analyst's domain knowledge, and in particular, her ability to translate that domain knowledge into relevant features

One Hot Encoding

For categorical variables where no such ordinal relationship exists, the integer encoding is not enough.

In fact, using this encoding and allowing the model to assume a natural ordering between categories may result in poor performance or unexpected results (predictions halfway between categories).
In this case, a one hot encoding can be applied to the integer representation. This is where the integer encoded variable is removed and a new binary variable is added for each unique integer value. In the color variable example, there are 3 categories and therefore 3 binary variables are needed. A 1 value is placed in the binary variable for the color and 0 values for the other colors.

The LSTM expects input data to have the dimensions: samples, time steps, and features. It is the second dimension of this input format, the time steps, that defines the number of time steps used for forward and backward passes on your sequence prediction problem

One Hot Encoding

For categorical variables where no such ordinal relationship exists, the integer encoding is not enough.

We are employing RNNs in production now which offers significant advantages over existing methods: reduced feature engineering; improved empirical performance; and better prediction explanations

When an analyst uses feature engineering to predict behavior, the performance of the model will depend greatly on the analyst's domain knowledge