HYPERPARAMETER TUNING
# How to tune Hyper parameters using Random Search in Python?

# How to tune Hyper parameters using Random Search in Python?

This recipe helps you tune Hyper parameters using Random Search in Python

This data science python source code does the following: 1. Different methods for Hyperparameter tuning a model. 2. Implements of RandomSearhCV using Cross Validation method. 3. Setting up parameters for RandomSearchCV. 4. Obtaining the best parameters and best result.

In [1]:

```
## How to tune Hyper-parameters using Random Search in Python
def Snippet_143():
print()
print(format('How to tune Hyper-parameters using Random Search in Python','*^82'))
import warnings
warnings.filterwarnings("ignore")
# load libraries
from scipy.stats import uniform
from sklearn import linear_model, datasets
from sklearn.model_selection import RandomizedSearchCV
# Load data
iris = datasets.load_iris()
X = iris.data
y = iris.target
# Create logistic regression
logistic = linear_model.LogisticRegression()
# Create Hyperparameter Search Space
# Create regularization penalty space
penalty = ['l1', 'l2']
# Create regularization hyperparameter distribution using uniform distribution
C = uniform(loc=0, scale=4)
# Create hyperparameter options
hyperparameters = dict(C=C, penalty=penalty)
# Create randomized search 5-fold cross validation and 100 iterations
clf = RandomizedSearchCV(logistic, hyperparameters, random_state=1, n_iter=100,
cv=5, verbose=0, n_jobs=-1)
# Fit randomized search
best_model = clf.fit(X, y)
# View best hyperparameters
print('Best Penalty:', best_model.best_estimator_.get_params()['penalty'])
print('Best C:', best_model.best_estimator_.get_params()['C'])
Snippet_143()
```

Machine Learning Project in R-Detect fraudulent click traffic for mobile app ads using R data science programming language.

Data Science Project in Python- Build a machine learning algorithm that automatically suggests the right product prices.

Data Science Project in Python- Given his or her job role, predict employee access needs using amazon employee database.

In this deep learning project, we will predict customer churn using Artificial Neural Networks and learn how to model an ANN in R with the keras deep learning package.

The goal of this data science project is to build a predictive model and find out the sales of each product at a given Big Mart store.

Data science project in R to develop automated methods for predicting the cost and severity of insurance claims.

Data Science Project in R-Predict the sales for each department using historical markdown data from the Walmart dataset containing data of 45 Walmart stores.

Text data requires special preparation before you can start using it for any machine learning project.In this ML project, you will learn about applying Machine Learning models to create classifiers and learn how to make sense of textual data.

In this data science project in R, we are going to talk about subjective segmentation which is a clustering technique to find out product bundles in sales data.

In this data science project, we will predict the credit card fraud in the transactional dataset using some of the predictive models.