Final answer:
Measuring a model's effectiveness on its training data can lead to overfitting and a false impression of predictive accuracy, which is why a separate testing set should be used to assess how well the model generalizes to new data.
Step-by-step explanation:
It is not advisable to measure a model's effectiveness on the same data it was trained on because doing so can lead to overfitting, where the model becomes too tailored to the specific data set and fails to generalize to new, unseen data. This can give a false impression of its predictive accuracy when in reality, the model has learned the noise and patterns specific to the training set rather than underlying general trends that would apply to other data sets.
Evaluating a model's effectiveness requires the use of a testing set, which is different from the training set. This approach enables us to assess how well the model generalizes to new data, which is crucial for its application outside of the training environment. Conservationists, statisticians, and data scientists alike rely on such unbiased evaluation to ensure they develop models that accurately predict and reflect real-world scenarios, without the confounding effects of overfitting.