Скачать или смотреть 8 3 hyperparameter tuning gridsearchcv and randomizedsearchcv

Download 1M+ code from https://codegive.com/cfb0859
hyperparameter tuning is an essential step in optimizing machine learning models. in this tutorial, we'll explore two popular methods for hyperparameter tuning: `gridsearchcv` and `randomizedsearchcv` from the `scikit-learn` library.

what are hyperparameters?

hyperparameters are configurations that are external to the model and cannot be learned from the training data. they are set before the training process begins and can significantly affect the performance of the model. examples include learning rate, number of trees in a random forest, and the maximum depth of a decision tree.

gridsearchcv

`gridsearchcv` exhaustively searches through a specified subset of hyperparameters for a given model. it evaluates all combinations of hyperparameters and selects the best one based on cross-validated performance.

pros and cons of gridsearchcv
**pros**:
simple to understand and implement.
guarantees finding the best combination of hyperparameters within the specified grid.

**cons**:
computationally expensive, especially with a large number of hyperparameters and values.
time-consuming as it evaluates every combination.

randomizedsearchcv

`randomizedsearchcv` samples a specified number of hyperparameter combinations from a provided grid. it evaluates a random selection of hyperparameters rather than all possible combinations.

pros and cons of randomizedsearchcv
**pros**:
more efficient than gridsearchcv when dealing with a large hyperparameter space.
can find a good combination quicker than exhaustively evaluating all possibilities.

**cons**:
there's no guarantee that the best combination will be found, as it only samples a subset of the space.

code example

in this example, we'll use the `randomforestclassifier` from scikit-learn to demonstrate both `gridsearchcv` and `randomizedsearchcv`.

step 1: import libraries



step 2: load data

let's use the iris dataset as an example.



step 3: define hyperparameter grid
...

#HyperparameterTuning #GridSearchCV #numpy
hyperparameter tuning
GridSearchCV
RandomizedSearchCV
machine learning
model optimization
cross-validation
parameter grid
search strategy
performance metrics
overfitting prevention
algorithm selection
training efficiency
model evaluation
hyperparameter selection
data science