In today’s fast-paced world of machine learning, automating model selection can be a game-changer for data scientists and analysts. Two underrated libraries that make this process easy are lazypredict and pycaret. These libraries automate the task of selecting the best machine learning models for your dataset, saving you time and effort.
Why Automate ML Model Selection?
Manual model selection is time-consuming and prone to errors. Trying each model can take days, and picking the wrong model early on can derail an entire project. Automation lets you quickly compare dozens of models, get performance metrics without writing repetitive code, and identify top-performing algorithms based on accuracy, F1 score, or RMSE.
Using Lazypredict
Lazypredict is a powerful library that automates machine learning model selection for datasets. It uses a range of algorithms to find the best fit for your data. After loading the dataset, lazypredict splits it into training and testing sets, then fits each algorithm to the data. The results are displayed in a clear format, showing the top-performing models.
Here’s an example code snippet that demonstrates how to use lazypredict:
“`python
import pandas as pd
from sklearn.model_selection import train_test_split
from lazypredict.Supervised import LazyClassifier
# Load dataset
url = “https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv”
df = pd.read_csv(url, header=None)
# Split data
X_train, X_test, y_train, y_test = train_test_split(df.iloc[:, :-1], df.iloc[:, -1], test_size=0.2, random_state=42)
# LazyClassifier
clf = LazyClassifier(verbose=0, ignore_warnings=True)
models, predictions = clf.fit(X_train, X_test, y_train, y_test)
# Top 5 models
print(models.head(5))
“`
Using PyCaret
PyCaret is another popular library that automates machine learning model selection. It uses a range of algorithms to find the best fit for your data and provides more detailed performance metrics.
Here’s an example code snippet that demonstrates how to use pycaret:
“`python
import pandas as pd
from sklearn.model_selection import train_test_split
from pycaret.classification import *
# Load dataset
url = “https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv”
df = pd.read_csv(url, header=None)
# Run 15+ models and evaluate them with cross-validation
clf = setup(data=df, target=df.columns[-1])
best_model = compare_models()
“`
Real-Life Use Cases
Automating machine learning model selection can be beneficial in various real-life scenarios:
* Rapid prototyping in hackathons
* Internal dashboards that suggest the best model for analysts
* Teaching ML without drowning in syntax
* Pre-testing ideas before full-scale deployment
Conclusion
Using AutoML libraries like lazypredict and pycaret can save time, improve decision-making, and boost productivity. These libraries provide a quick feedback loop, allowing you to focus on feature engineering, domain knowledge, and interpretation.
Source: https://towardsdatascience.com/how-i-automated-my-machine-learning-workflow-with-just-10-lines-of-python