REGRESSION EXAMPLES
DATA CLEANING PYTHON
DATA MUNGING
MACHINE LEARNING RECIPES
PANDAS CHEATSHEET
ALL TAGS
# How to create and optimize a baseline Lasso Regression model?

# How to create and optimize a baseline Lasso Regression model?

This recipe helps you create and optimize a baseline Lasso Regression model

Many a times while working on a dataset and using a Machine Learning model we don"t know which set of hyperparameters will give us the best result. Passing all sets of hyperparameters manually through the model and checking the result might be a hectic work and may not be possible to do.

To get the best set of hyperparameters we can use Grid Search. Grid Search passes all combinations of hyperparameters one by one into the model and check the result. Finally it gives us the set of hyperparemeters which gives the best result after passing in the model.

So this recipe is a short example of how we can create and optimize a baseline Lasso regression model.

```
from sklearn import decomposition, datasets
from sklearn import linear_model
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV, cross_val_score
from sklearn.preprocessing import StandardScaler
```

Here we have imported various modules like decomposition, datasets, linear_model, Pipeline, StandardScaler and GridSearchCV from differnt libraries. We will understand the use of these later while using it in the in the code snipet.

For now just have a look on these imports.

Here we have used datasets to load the inbuilt boston dataset and we have created objects X and y to store the data and the target value respectively.
```
dataset = datasets.load_boston()
X = dataset.data
y = dataset.target
```

StandardScaler is used to remove the outliners and scale the data by making the mean of the data 0 and standard deviation as 1. So we are creating an object std_scl to use standardScaler.
```
std_slc = StandardScaler()
```

We are also using Principal Component Analysis(PCA) which will reduce the dimension of features by creating new features which have most of the varience of the original data.
```
pca = decomposition.PCA()
```

Here, we are using Lasso Regression as a Machine Learning model to use GridSearchCV. So we have created an object lasso.
```
lasso = linear_model.Lasso()
```

Pipeline will helps us by passing modules one by one through GridSearchCV for which we want to get the best parameters. So we are making an object pipe to create a pipeline for all the three objects std_scl, pca and lasso.
```
pipe = Pipeline(steps=[("std_slc", std_slc),
("pca", pca),
("lasso", lasso)])
```

Now we have to define the parameters that we want to optimise for these three objects.

StandardScaler doesnot requires any parameters to be optimised by GridSearchCV.

Principal Component Analysis requires a parameter "n_components" to be optimised. "n_components" signifies the number of components to keep after reducing the dimension.
```
n_components = list(range(1,X.shape[1]+1,1))
```

Logistic Regression requires two parameters "normalize" and "selection" to be optimised by GridSearchCV. So we have set these two parameters as a list of values form which GridSearchCV will select the best value of parameter.
```
normalize = [True, False]
selection = ["cyclic", "random"]
```

Now we are creating a dictionary to set all the parameters options for different modules.
```
parameters = dict(pca__n_components=n_components,
lasso__normalize=normalize,
lasso__selection=selection)
```

Before using GridSearchCV, lets have a look on the important parameters.

- estimator: In this we have to pass the models or functions on which we want to use GridSearchCV
- param_grid: Dictionary or list of parameters of models or function in which GridSearchCV have to select the best.
- Scoring: It is used as a evaluating metric for the model performance to decide the best hyperparameters, if not especified then it uses estimator score.

```
clf = GridSearchCV(pipe, parameters)
clf.fit(X, y)
```

Now we are using print statements to print the results. It will give the values of hyperparameters as a result.
```
print("Best Number Of Components:", clf.best_estimator_.get_params()["pca__n_components"])
print(clf.best_estimator_.get_params()["lasso"])
CV_Result = cross_val_score(clf, X, y, cv=10, n_jobs=-1, scoring="r2")
print(CV_Result)
print(CV_Result.mean())
print(CV_Result.std())
```

As an output we get:
Best Number Of Components: 5 Lasso(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=1000, normalize=False, positive=False, precompute=False, random_state=None, selection="cyclic", tol=0.0001, warm_start=False) [ 0.62165111 0.63662955 -0.5470779 0.38231819 0.47040916 0.479514 0.0981076 0.1841878 -0.73314519 0.50955313] 0.21021474338674118 0.45686116902394064

In this ensemble machine learning project, we will predict what kind of claims an insurance company will get. This is implemented in python using ensemble machine learning algorithms.

In this machine learning project, you will uncover the predictive value in an uncertain world by using various artificial intelligence, machine learning, advanced regression and feature transformation techniques.

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.

Machine Learning Project in R- Predict the customer churn of telecom sector and find out the key drivers that lead to churn. Learn how the logistic regression model using R can be used to identify the customer churn in telecom dataset.

In this data science project, you will work with German credit dataset using classification techniques like Decision Tree, Neural Networks etc to classify loan applications using R.

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.

Deep Learning Project- Learn to apply deep learning paradigm to forecast univariate time series data.

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

In this project, we are going to talk about Time Series Forecasting to predict the electricity requirement for a particular house using Prophet.

In this supervised learning machine learning project, you will predict the availability of a driver in a specific area by using multi step time series analysis.