LambdaMart | Python implementation of LambdaMart | Machine Learning library

LambdaMart is a Python library typically used in Artificial Intelligence, Machine Learning, Numpy applications. LambdaMart has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However LambdaMart build file is not available. You can download it from GitHub.

Python implementation of LambdaMart. LambdaMART API: LambdaMART(training_data=None, number_of_trees=0, leaves_per_tree=0, learning_rate=0). To start using the API, you need to include the files: “lambdamart.py” and “RegressionTree.py” in the same directory. Create a Python file in the same directory as the other Python files and for the sake of this tutorial, call it “example.py”. To run this example, you will need a training dataset and a test dataset. You can download the training dataset here and the test dataset here: #Step 1: Import needed packages In the “example.py” file, you will need to import lambdamart and numpy to pass in the data in the correct format like below: from lambdamart import LambdaMART import numpy as np. #Step 2: Create a function to pass in the data properly from the given training and test datasets. #Step 3: Call the get_data function to get the training and test datasets. Also put it in a main function. Please replace the “<>” and the contents in them with the appropriate file locations. #Step 4: Call LambdaMART, fit the data, and put it under the main function. Please note that you can set the parameters to your specifications. Also note that the higher number of trees will make the program slower. #Step 5: Run prediction or validation and put it under the main function. Please note that predicted_scores from predict and validate are the same. Also the predict function cannot contain the relevance score, so that column needs to be omitted like is has been done above.