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| − | ==Introduction== | + | <gallery mode="packed"> |
| − | A supervised learning classification task is an example of a machine-learning task. This type of problem requires that a model be trained on a labeled dataset. Each pair consists of an input object (such an image) as well as a label (such the object's classification). The model is then used to predict the label of unknown input objects.
| + | File:ZYBER_Charging_Dock_for_Meta_Quest_2_image1.jpg |
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| − | ==The Iris Dataset==
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| − | The [[Iris flowers data set]] is a well-known example of a classification issue. The data set includes 150 samples of iris flowers. Each sample has four features: the sepal width and length, and the length and width of the petals. The samples are from one of three classes: [[Iris vericolor]], [[Iris virginica]], and [[Iris setosa]. It is necessary to build a model from the data to predict the class of new Iris flowers based on their features.
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| − | ==Support Vector Machine (SVM)==
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| − | Support Vector Machine]] algorithm. The SVM algorithm determines the best decision boundary (also called a hyperplane) to separate the different classes within the feature space. The decision boundary on which new input objects fall determines how they are classified.
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| − | ==Example Implementation in Python==
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| − | Python]]:
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| − | ```python
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| − | From sklearn Import Datasets
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| − | From sklearn import Svm
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| − | # Load the Iris dataset
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| − | iris = datasets.load_iris()
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| − | X = iris.data
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| − | y = iris.target
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| − | # Create an SVM Classifier with a Linear Kernel
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| − | clf = svm.SVC(kernel='linear')
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| − | # Train the classifier using the iris dataset
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| − | clf.fit(X, y)
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| − | # Predict the class and color of a new, unidentified iris
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| − | new_iris = [[5.0, 3.6, 1.3, 0.25]]
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| − | predicted_class = clf.predict(new_iris)
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| − | # Print the predicted class
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| − | print(predicted_class)
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| − | ```
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| − | This code would output the class for the new iris, such as'setosa.
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| − | ==Explain Like I'm 5 (ELI5)==
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| − | Machine learning allows computers to learn without having to be programmed. One example is a game in which the computer attempts to identify what kind of flower it is seeing based on photos of other flowers it has seen. The computer is shown pictures of different flowers and is told what kind of flower it is looking at. After looking at many examples, the computer can see a new photo of a flower to determine what type it is.
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