# Parameters of model can be changed by using the attributes ending with an underscore For this purpose we have to create an estimator and then call its fit method. Now we have loaded data, let’s try learning from it and predict on new data. We are printing shape of data for ease, you can also print whole data if you wish so, running the codes gives an output like this: Scikit Learn SVM - Learning and Predicting # Print shape of data to confirm data is loaded Let’s see how to load the dataset using scikit-learn.

It is a dataset of a flower, it contains 150 observations about different measurements of the flower. Let’s start with loading a dataset to play with. Once you are done with the installation, you can use scikit-learn easily in your Python code by importing it as: import sklearn If you like conda, you can also use the conda for package installation, run the following command: conda install scikit-learn For pip installation, run the following command in the terminal: pip install scikit-learn Once we have these packages installed we can proceed with the installation. Scikit assumes you have a running Python 2.7 or above platform with NumPY (1.8.2 and above) and SciPY (0.13.3 and above) packages on your device. It is open source and released under BSD license. Scikit-learn is used to build models and it is not recommended to use it for reading, manipulating and summarizing data as there are better frameworks available for the purpose. Scikit is written in Python (most of it) and some of its core algorithms are written in Cython for even better performance.

It gets its name from “Scikit”, a separate third-party extension to SciPy. The scikit-learn project kicked off as a Google Summer of Code (also known as GSoC) project by David Cournapeau as scikits.learn. It is designed to work with Python Numpy and SciPy. It features several regression, classification and clustering algorithms including SVMs, gradient boosting, k-means, random forests and DBSCAN. Scikit-learn is a machine learning library for Python.

Conda install sklearn