FLAML Tutorial in Python

FLAML is an automatic machine learning library created by Microsoft for fast and lightweight automatic machine learning. It stands for Fast and Lightweight AutoML. If you have never used any AutoML library in Python for machine learning, then this article is for you. In this article, I will take you through a tutorial on the FLAML library in Python.

FLAML

FLAML stands for Fast and Lightweight AutoML. It is an automatic machine learning library in Python created by Microsoft. It can help data scientists to find and train accurate machine learning models automatically for both classification and regression problems. Some of the advantages of using this library for machine learning are:

1. It can find and train accurate machine learning models for both classification and regression with very few computational resources;
2. The developers are free to choose their desired customizability;
3. Allows human guidance in hyperparameter tuning;
4. It also has support for automatic hyperparameter tuning for online machine learning algorithms.

If you have never used the FLAML library in Python, then you can easily install it by using the pip command on your terminal or command prompt as mentioned below:

1. pip install flaml
2. pip install flaml[notebook]

If you want to install this AutoML library in your Python environment, you must use the first command, and if you want to install it on a Jupyter or Google Colab notebook, you must use the second command. Now in the section below, I will introduce you to a tutorial on the FLAML library in Python by showing you an example.

FLAML Tutorial using Python

I hope you now have understood the FLAML library in Python. Now let’s have a look at an example of using this AutoML library for classification. I will start this task by importing the necessary Python libraries and the dataset that we need for this task:

Now let’s prepare the data to fit into the model and train a classification model using this AutoML library:

Now below is how you can predict by using the trained model:

news_headline = "Cow dung can cure Corona Virus"
data = cv.transform([news_headline]).toarray()
print(automl.predict(data))

['FAKE']

Summary

So this is how you can use the Fast and Lightweight AutoML library in Python for training accurate machine learning models. It stands for Fast and Lightweight AutoML. It is an automatic machine learning library in Python created by Microsoft. I hope you liked this article on a tutorial on the FLAML library in Python. Feel free to ask your valuable questions in the comments section below.