The algorithm terminates either when all the attributes have been exhausted, or the decision tree perfectly classifies the examples. The following diagram should explain the ID3 algorithm further: 6.3 ...

Because the decision tree model was trained using normalized and encoded data, the x-input must be normalized and encoded in the same way. Notice the double square brackets on the x-input. The predict ...

To predict the y value for an input of X = (0.17, 0.96, 0.44), the algorithm starts at the root node. If the X element at the split column ([1]) is less than or equal to the split value (0.64), a ...

Decision trees start with broad characteristics and progressively narrow the focus onto specific independent variables that relate to the target, for example, high cumulative production gas wells.