Iris-Flower-Classification-Project

Certainly! Let's break down the key points into a set of concise, point-wise answers:

1. IRIS Flower Classification:

   • Refers to a dataset known as the Iris dataset.
   • Contains measurements for four features (sepal length, sepal width, petal length, and petal width) of 150 iris flowers.
   • The dataset is commonly used for machine learning tasks.

2. Predict Flowers based on Specific Features:

   • The task involves predicting the type or species of iris flowers.
   • Prediction is based on specific features such as sepal length, sepal width, petal length, and petal width.

3. Model Class Membership Probabilities:

   • Rather than predicting a hard label, the goal is to predict the probabilities of the flower belonging to each class.
   • Classification algorithms are used to output probability distributions over the different flower species.

4. Conditioned on Flower Features:

   • The prediction of class membership probabilities is dependent on the specific features of the flower.
   • During training, the algorithm learns patterns and relationships between these features and the corresponding class labels.