How to use LIGHTGBM classifier work in python?

How to use LIGHTGBM classifier work in python?

How to use LIGHTGBM classifier work in python?

This recipe helps you use LIGHTGBM classifier work in python

Recipe Objective

LightGBM is a gradient boosting framework that uses tree-based learning algorithms. LightGBM classifier helps while dealing with classification problems.

So this recipe is a short example on How to use LIGHTGBM classifier work in python. Let's get started.

Step 1 - Import the library

from sklearn import datasets from sklearn import metrics from sklearn.model_selection import train_test_split from sklearn.datasets import load_iris import lightgbm as ltb

Let's pause and look at these imports. We have exported train_test_split which helps in randomly breaking the datset in two parts. Here sklearn.dataset is used to import one classification based model dataset. Also, we have exported lightgbm (It might not be available with anaconda package and therefore might be needed to install manually).

Step 2 - Setup the Data

X,y=load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)

Here, we have used load_iris function to import our dataset in two list form (X and y) and therefore kept return_X_y to be True. Further with have broken down the dataset into 2 parts, train and test with ratio 3:4.

Now our dataset is ready.

Step 3 - Building the model

model = ltb.LGBMClassifier()

We have simply built a classification model with LGBMClassifer with default values.

Step 4 - Fit the model and predict for test set, y_train) expected_y = y_test predicted_y = model.predict(X_test)

Here we have simply fit used fit function to fit our model on X_train and y_train. Now, we are predicting the values of X_test using our built model.

Step 5 - Printing the results

print(metrics.classification_report(expected_y, predicted_y)) print(metrics.confusion_matrix(expected_y, predicted_y))

Here we have trying to analyze the model built and its efficiency on the predicted value of X_test and y_test.

Step 6 - Lets look at our dataset now

Once we run the above code snippet, we will see:

Scroll down the ipython file to have a look at the results.

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