The Flaw of GridSearchCV
Before using GridSearchCV, note that KNeighborsClassifier has more hyperparameters than n_neighbors. Two important ones are weights and p.
Weights
By default, the classifier uses weights='uniform', meaning all k neighbors vote equally.
Setting weights='distance' gives closer neighbors more influence, often improving predictions when nearby points are more relevant.
P
The p parameter controls the distance metric:
p=1: Manhattan distance;p=2: Euclidean distance.
A p parameter can take any positive integer. There are many different distances, but they are harder to visualize than p=1 or p=2.
Do not worry if the details of weights or p are unclear. They are introduced simply to show that there is more than one hyperparameter that can influence the modelβs predictions. Treat them as examples of hyperparameters that can be tuned.
Previously, only n_neighbors was tuned. To search over all three hyperparameters, use:
param_grid = {
'n_neighbors': [1, 3, 5, 7],
'weights': ['distance', 'uniform'],
'p': [1, 2]
}
GridSearchCV tries all the possible combinations to find the best, so it will try all of those:
A larger grid like:
param_grid = {
'n_neighbors': [...],
'weights': ['distance', 'uniform'],
'p': [1, 2, 3, 4, 5]
}
creates 100 combinations. With 5-fold cross-validation, the model is trained 500 times. This is fine for small datasets, but for bigger ones it becomes too slow.
To reduce computation time, RandomizedSearchCV tests only a random subset of combinations, usually finding strong results much faster than a full grid search.
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Completion rate improved to 3.13The Flaw of GridSearchCV
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Before using GridSearchCV, note that KNeighborsClassifier has more hyperparameters than n_neighbors. Two important ones are weights and p.
Weights
By default, the classifier uses weights='uniform', meaning all k neighbors vote equally.
Setting weights='distance' gives closer neighbors more influence, often improving predictions when nearby points are more relevant.
P
The p parameter controls the distance metric:
p=1: Manhattan distance;p=2: Euclidean distance.
A p parameter can take any positive integer. There are many different distances, but they are harder to visualize than p=1 or p=2.
Do not worry if the details of weights or p are unclear. They are introduced simply to show that there is more than one hyperparameter that can influence the modelβs predictions. Treat them as examples of hyperparameters that can be tuned.
Previously, only n_neighbors was tuned. To search over all three hyperparameters, use:
param_grid = {
'n_neighbors': [1, 3, 5, 7],
'weights': ['distance', 'uniform'],
'p': [1, 2]
}
GridSearchCV tries all the possible combinations to find the best, so it will try all of those:
A larger grid like:
param_grid = {
'n_neighbors': [...],
'weights': ['distance', 'uniform'],
'p': [1, 2, 3, 4, 5]
}
creates 100 combinations. With 5-fold cross-validation, the model is trained 500 times. This is fine for small datasets, but for bigger ones it becomes too slow.
To reduce computation time, RandomizedSearchCV tests only a random subset of combinations, usually finding strong results much faster than a full grid search.
Thanks for your feedback!
