Скачать или смотреть Train & Evaluate a Machine Learning Model in Python with AI 🚀

Hey everyone! 🌟 Today, we’ll train and evaluate a Machine Learning model in Python in under a minute! 🕒 We’re using scikit-learn to train a Random Forest Classifier on the classic Iris dataset, and we’ll also evaluate its accuracy. Let’s break it down:

Import Libraries:
We import load_iris, train_test_split, RandomForestClassifier, and now accuracy_score to measure model performance.
Load Data:
Load the Iris dataset, which contains data on three different flower species. X are the features and y are the labels.
Split Data:
Split into training (80%) and testing (20%) datasets to evaluate the model’s performance later on.
Train Model:
We use a Random Forest Classifier to train on our training set (X_train and y_train).
Make Predictions:
Make predictions using the test set (X_test).
Measure Accuracy:
Finally, evaluate the model’s performance using accuracy_score. This gives us a percentage of how many predictions were correct out of the total.
The result is printed as the Model Accuracy. This helps you understand how well the model is performing. In this case, we might get an accuracy of around 95% or higher—pretty good for a simple model!

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