Iris species classification
We predict the species of iris plant based on its physical characteristics such as sepal length, sepal width, petal length, and petal width.
The Iris species classification prediction problem is a supervised learning problem where the goal is to predict the species of iris plant based on its physical characteristics such as sepal length, sepal width, petal length, and petal width.
A common solution to this problem is to use a classification algorithm, such as k-Nearest Neighbors (k-NN), Decision Tree, Random Forest, or Support Vector Machine (SVM). The model is trained using a labeled dataset of iris plants, where the species of each plant is known.
In the training process, the model learns the relationship between the physical characteristics of the iris plants and their species. Once the model is trained, it can be used to predict the species of a new, previously unseen iris plant based on its physical characteristics.
To evaluate the performance of the model, various metrics such as accuracy, precision, recall, and F1 score can be used. In addition, cross-validation techniques can be used to validate the model and prevent overfitting.
It's important to note that this is a relatively simple problem, with a relatively small and balanced dataset, making it a popular problem for beginner machine learning practitioners to start with.