#Install weka python install
#Install weka python generator
#Install weka python code

LetвЂ™s see the example for cross validating the J48 classifier on the dataset and the result of specific statistics, > remove = Filter (classname='.Remove', options=) The filter class we get from the weka.filters module which agree to filter the datasets, for example to remove the last attributes by using the Remove filter, >converters.save_any_file(data,'') Filters Those methods decides the loader and saver based on the file extension, The module has easy methods for storing and loading the datasets called the load_any_file and save_any_file.

#Install weka python code

To load and save the datasets of different data formats we can make use of the Loader and Saver classes, letвЂ™s see the following code loads an ARFF file and save it in CSV, > DataGenerator.make_data (generator, ) Loaders and Savers

#Install weka python generator

> generator = DataGenerator (classname= '.Agrawal', options= ) > from weka.datagenerators import DataGenerator > Cls = Classifier (classname = '48', options= )Д«y using WekaвЂ™s Data Generators we can generate Artificial data for example Agrawal classification generator, > cls=Classifier(classname='48', options=)Д«y using the option property we can also get the current set options, as follows > cls= Classifier(classname = 'weka.classifiers. > from weka.classifiers import Classifier There are two examples to instantiate a J48 classifier one is used for option property and another is for shortcut for constructor, The Option-Handling derived from OptionHandler itвЂ™s the module of it allows to get and set the options via property options. In the end we have to stop the JVM, for that follows the code as below Generally we have to increase the size of heap maximum for JVM, for that purpose we need to reserves as 512 MB, like start (packages='/my/packages/are/somewhere/else') If the Weka home directory is not available in wekafiles, then we have to specify in two options for alternative locations one is to make use of the WEKA_HOME environment variable or to make use the packages parameter to supplying the directory, letвЂ™s see the code below as follows > jvm.start (system_cp= True, packages= True) we want to call the ClassPath variables and to make use of the installed packages in Weka we need to use the following code to call, Petuum.ДЄ/ml Play with Tensorflow playground. Vowpal Wabbit Microsoft Research and (previously) Yahoo! Research Fast and scalable tool for learning linear model.

An implementation of Support Vector Machines (SVMs) in C. SVMlight Thorsten Joachims, Cornell University. and Lin, C.J., A practical guide to support vector. Simple and easy-to-use support vector machines tool. NEVER USE YOUR TEST DATA FOR TUNINGДЎ6 Resources LIBSVM and LIBLINEAR SVMlight Vowpal WabbitД¬hih-Jen Lin, National Taiwan University. fit_intercept: solve: lbfgs, lblinear, netwon-cg. Petal width.ДЎ2 Cross Validation Training Testing Train-test splitДЎ3 0 Fold Train Accuracy:, Test Accuracy: 1 Fold Train Accuracy:, Test Accuracy: 2 Fold Train Accuracy:, Test Accuracy: 3 Fold Train Accuracy:, Test Accuracy: 4 Fold Train Accuracy: ,ДЎ4 From Logistic Regression to Support Vector MachineД Fold Train Accuracy:, Test Accuracy: 1 Fold Train Accuracy:, 2 Fold Train Accuracy:, 3 Fold Train Accuracy:, Test Accuracy: 4 Fold Train Accuracy:, Test Accuracy: P.S.: from sklearn.svm import SVCДЎ5 Parameter Tuning NEVER USE YOUR TEST DATA FOR TUNING Д¬: Penalty parameter. Willi Richert, Luis Pedro Coelho: Cross Validated!ДЁ Dataset UC Irvine (UCI) Machine Learning Repository Iris dataset: Classify the flowersвЂ™ species using the following features. Python official tutorial: Stackoverflow! Machine Learning: Building Machine Learning Systems with Python. For those who favors an IDE, Pењњharm is a powerful IDE for Python and scientific development.

#Install weka python install

Install Python distribution, such as Anaconda. Getting started with Scikit-learn:ДҐ Installing Python Install Python, NumPy, SciPy, Scikit-learning, and etc. Matplotlib: One of the most convenient library to plot high-quality graph using Python.Д¤ Scikit-learn Scikit-learn is a marvelous machine learning toolkit in Python. NumPy and SciPy: Highly optimized storage and operation for multidimensional arrays, which are the basis data structure of most state-of-the-art algorithms.

Presentation on theme: 'Building Machine Learning System with Python'- Presentation transcript:ДЎ Building Machine Learning System with Python