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Grid Search

Machine Learning Algorithms play a big role in data-driven decision-making. Professionals never rely on a single algorithm to solve a business challenge. Based on the problem, one always applies various relevant algorithms and picks the optimal model based on the models’ best performance metrics. But this isn’t the end of the story. Hyperparameters can be used to improve model performance. Finding the appropriate hyperparameters will therefore assist us in achieving the highest-performing model. We’ll learn about Hyperparameters, Grid Search, Cross-Validation, GridSearchCV, and tweaking Hyperparameters in Python in this tutorial.

Manual Search, Grid Search, Random Search, Grid Search, and Bayesian Optimizations can be used to choose hyperparameters for a model.

Parameters and hyperparameters

Both parameters and hyperparameters are part of the ML model, although they serve different purposes. Let’s look at the differences between them in the context of Machine Learning.

Parameters are the variables that the Machine Learning algorithm uses to forecast results based on historical data input. The Machine Learning method itself uses an optimization approach to estimate these. As a result, neither the user nor the expert can set or hard-code these variables. These variables are used in the model training process.

Hyperparameters are variables that the user specifies throughout the Machine Learning model construction process. As a result, hyperparameters are provided before parameters, or we may say that hyperparameters are utilized to assess the model’s ideal parameters. The nice thing about hyperparameters is that the user who is developing the model decides on their values.


Now that we understand what hyperparameters are, our objective should be to identify the ideal hyperparameter values to obtain the best prediction results from our model. However, the question of how to identify the optimal sets of hyperparameters emerges. The Manual Search technique may be used to locate the optimal hyperparameters by utilizing a hit-and-miss approach, which would take a long time to develop a single model.

As a result, technologies like Random Search and GridSearch were developed. In this section, we’ll go over how Grid Search works and how GridSearchCV handles cross-validation.

Grid Search calculates the performance for each combination of all the supplied hyperparameters and their values and then chooses the optimum value for the hyperparameters. Based on the number of hyperparameters involved, this makes the processing time-consuming and costly.

GridSearch and Cross-validation

The model is trained using cross-validation. As we all know, we divide the data into two pieces before training the model with it: train data and test data. The procedure of cross-validation splits the train data into two parts: the train data and the validation data.

K-fold Cross-validation is the most common kind of cross-validation. The training data is divided into k divisions using an iterative approach. One division is kept for testing and the remaining k-1 partitions are used to train the model in each iteration. In the following iteration, the next partition will be used as test data, and the remaining k-1 will be used as train data, and so on. It will record the model’s performance in each iteration and offer the average of all the results in the end. As a result, it is a time-consuming operation.

GridSearch is a model evaluation phase that should be completed once the Data Processing activities have been completed. Comparing the results of Tuned and Untuned Models is always a good idea. This will take time and money, but it will undoubtedly yield the finest results. If you need assistance, the scikit-learn API is a fantastic place to start. Learning by doing is always beneficial.


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