Tutorials References Exercises Videos Menu
Free Website Get Certified Upgrade

Python Tutorial

Python HOME Python Intro Python Get Started Python Syntax Python Comments Python Variables Python Data Types Python Numbers Python Casting Python Strings Python Booleans Python Operators Python Lists Python Tuples Python Sets Python Dictionaries Python If...Else Python While Loops Python For Loops Python Functions Python Lambda Python Arrays Python Classes/Objects Python Inheritance Python Iterators Python Scope Python Modules Python Dates Python Math Python JSON Python RegEx Python PIP Python Try...Except Python User Input Python String Formatting

File Handling

Python File Handling Python Read Files Python Write/Create Files Python Delete Files

Python Modules

NumPy Tutorial Pandas Tutorial SciPy Tutorial Django Tutorial

Python Matplotlib

Matplotlib Intro Matplotlib Get Started Matplotlib Pyplot Matplotlib Plotting Matplotlib Markers Matplotlib Line Matplotlib Labels Matplotlib Grid Matplotlib Subplot Matplotlib Scatter Matplotlib Bars Matplotlib Histograms Matplotlib Pie Charts

Machine Learning

Getting Started Mean Median Mode Standard Deviation Percentile Data Distribution Normal Data Distribution Scatter Plot Linear Regression Polynomial Regression Multiple Regression Scale Train/Test Decision Tree Confusion Matrix Hierarchical Clustering Logistic Regression Grid Search Categorical Data K-means Bootstrap Aggregation Cross Validation AUC - ROC Curve K-nearest neighbors

Python MySQL

MySQL Get Started MySQL Create Database MySQL Create Table MySQL Insert MySQL Select MySQL Where MySQL Order By MySQL Delete MySQL Drop Table MySQL Update MySQL Limit MySQL Join

Python MongoDB

MongoDB Get Started MongoDB Create Database MongoDB Create Collection MongoDB Insert MongoDB Find MongoDB Query MongoDB Sort MongoDB Delete MongoDB Drop Collection MongoDB Update MongoDB Limit

Python Reference

Python Overview Python Built-in Functions Python String Methods Python List Methods Python Dictionary Methods Python Tuple Methods Python Set Methods Python File Methods Python Keywords Python Exceptions Python Glossary

Module Reference

Random Module Requests Module Statistics Module Math Module cMath Module

Python How To

Remove List Duplicates Reverse a String Add Two Numbers

Python Examples

Python Examples Python Compiler Python Exercises Python Quiz Python Certificate

Machine Learning - Grid Search


On this page, W3schools.com collaborates with NYC Data Science Academy, to deliver digital training content to our students.


Grid Search

The majority of machine learning models contain parameters that can be adjusted to vary how the model learns. For example, the logistic regression model, from sklearn, has a parameter C that controls regularization,which affects the complexity of the model.

How do we pick the best value for C? The best value is dependent on the data used to train the model.


How does it work?

One method is to try out different values and then pick the value that gives the best score. This technique is known as a grid search. If we had to select the values for two or more parameters, we would evaluate all combinations of the sets of values thus forming a grid of values.

Before we get into the example it is good to know what the parameter we are changing does. Higher values of C tell the model, the training data resembles real world information, place a greater weight on the training data. While lower values of C do the opposite.


Using Default Parameters

First let's see what kind of results we can generate without a grid search using only the base parameters.

To get started we must first load in the dataset we will be working with.

from sklearn import datasets
iris = datasets.load_iris()

Next in order to create the model we must have a set of independent variables X and a dependant variable y.

X = iris['data']
y = iris['target']

Now we will load the logistic model for classifying the iris flowers.

from sklearn.linear_model import LogisticRegression

Creating the model, setting max_iter to a higher value to ensure that the model finds a result.

Keep in mind the default value for C in a logistic regression model is 1, we will compare this later.

In the example below, we look at the iris data set and try to train a model with varying values for C in logistic regression.

logit = LogisticRegression(max_iter = 10000)

After we create the model, we must fit the model to the data.

print(logit.fit(X,y))

To evaluate the model we run the score method.

print(logit.score(X,y))

Example

from sklearn import datasets
from sklearn.linear_model import LogisticRegression

iris = datasets.load_iris()

X = iris['data']
y = iris['target']

logit = LogisticRegression(max_iter = 10000)

print(logit.fit(X,y))

print(logit.score(X,y))
Run example »

With the default setting of C = 1, we achieved a score of 0.973.

Let's see if we can do any better by implementing a grid search with difference values of 0.973.


ADVERTISEMENT


Implementing Grid Search

We will follow the same steps of before except this time we will set a range of values for C.

Knowing which values to set for the searched parameters will take a combination of domain knowledge and practice.

Since the default value for C is 1, we will set a range of values surrounding it.

C = [0.25, 0.5, 0.75, 1, 1.25, 1.5, 1.75, 2]

Next we will create a for loop to change out the values of C and evaluate the model with each change.

First we will create an empty list to store the score within.

scores = []

To change the values of C we must loop over the range of values and update the parameter each time.

for choice in C:
  logit.set_params(C=choice)
  logit.fit(X, y)
  scores.append(logit.score(X, y))

With the scores stored in a list, we can evaluate what the best choice of C is.

print(scores)

Example

from sklearn import datasets
from sklearn.linear_model import LogisticRegression

iris = datasets.load_iris()

X = iris['data']
y = iris['target']

logit = LogisticRegression(max_iter = 10000)

C = [0.25, 0.5, 0.75, 1, 1.25, 1.5, 1.75, 2]

scores = []

for choice in C:
  logit.set_params(C=choice)
  logit.fit(X, y)
  scores.append(logit.score(X, y))

print(scores)
Run example »

Results Explained

We can see that the lower values of C performed worse than the base parameter of 1. However, as we increased the value of C to 1.75 the model experienced increased accuracy.

It seems that increasing C beyond this amount does not help increase model accuracy.


Note on Best Practices

We scored our logistic regression model by using the same data that was used to train it. If the model corresponds too closely to that data, it may not be great at predicting unseen data. This statistical error is known as over fitting.

To avoid being misled by the scores on the training data, we can put aside a portion of our data and use it specifically for the purpose of testing the model. Refer to the lecture on train/test splitting to avoid being misled and overfitting.