Logistic regression computes one weighted sum of the NorthPeak columns and turns it into a probability. A neural network does that many times, in layers, and can learn shapes a straight line cannot, such as "high vibration matters only when the load is low". MLPClassifier(hidden_layer_sizes=(32, 16)) has 1,025 weights to learn, against 15 for the logistic regression. This lesson explains layers, epochs and loss, then trains that network on the scaled Week 4 features, where it reaches an AUC of 0.945 on the test quarter.
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