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Example : my data doesn't fit into memory can I do :
model=my_model
for i in range(20)
model.fit(X_i,Y_i)
This will delete the first 19 fit. and keep only the last one. How can I avoid this? Can I retrain a model saved and loaded? Thank you
791
fit(X_train, y_train) for a second time - it'll overwrite all previously fitted coefficients, weights, intercept (bias), etc. Some estimators (having the warm_start parameter) will reuse the solutions from the previous calls to fit() as initial solution in new call when warm_start = True .
Yes, successive calls to fit will incrementally train the model. So, yes, you can call fit multiple times.
The fit() method takes the training data as arguments, which can be one array in the case of unsupervised learning, or two arrays in the case of supervised learning.
Model fitting is a measure of how well a machine learning model generalizes to similar data to that on which it was trained. A model that is well-fitted produces more accurate outcomes. A model that is overfitted matches the data too closely. A model that is underfitted doesn't match closely enough.
Some models have a "warm_start" parameter, where it will initialise model parameters with the previous solution from fit()
See for instance SGDClassifier
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