Hits: 791

# IRIS Flower Classification using Logistic Regression Classifier

IRIS flower classification is a common problem in machine learning. The IRIS dataset is a well-known dataset that contains information about different types of IRIS flowers, including their sepal length, sepal width, petal length, and petal width. The goal of IRIS flower classification is to use this information to correctly identify the type of IRIS flower.

One way to classify IRIS flowers is to use a logistic regression classifier. Logistic regression is a type of machine learning algorithm that can be used for classification problems. It tries to find a relationship between the input features and the output by using a logistic function.

In Python, you can use the scikit-learn library to train and evaluate a logistic regression classifier for IRIS flower classification.

First, you’ll need to install scikit-learn if you don’t already have it. You can do this by running `pip install scikit-learn`

in your command line.

Next, you’ll need to import the necessary libraries and load the IRIS dataset.

Now, you can create the logistic regression classifier, fit the model to the training data and evaluate it with test data.

```
from sklearn.linear_model import LogisticRegression
log_reg = LogisticRegression(max_iter=10000)
log_reg.fit(X_train, y_train)
from sklearn.metrics import accuracy_score
print("Test accuracy: {:.2f}".format(accuracy_score(log_reg.predict(X_test), y_test)))
```

The logistic regression classifier takes in the training data and learns from it to make predictions. The accuracy_score function then compares the predictions with the actual IRIS types from the test data. This is how you can use logistic regression classifier to classify IRIS flowers using Python.

In this Machine Learning Recipe, you will learn: How to do IRIS Flower Classification using Logistic Regression Classifier.

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause.The information presented here could also be found in public knowledge domains.