Support Vector Machine (Classification) - Step by Step
- 1. Data Science & Machine Learning Prepared by – Manish IT.mnchoudhary@gmail.com Data Science & Machine Learning Step by Step : Support Vector Machine Date 28th Nov
- 2. Data Science Prepared by Manish IT.mnchoudhary@gmail.com Classification Model : SVM - Linear In Python, what is the class used to create a logistic regression classifier ? • SVM Linear Linearly separate the data points Support Vector Machine” (SVM) is a supervised machine learning algorithm which can be used for both classification or regression challenges. However, it is mostly used in classification problems. In this algorithm, we plot each data item as a point in n-dimensional space (where n is number of features you have) with the value of each feature being the value of a particular coordina Then, we perform classification by finding the hyper-plane that differentiate the two classes very well (look at the below snapshot).
- 3. Data Science Prepared by Manish IT.mnchoudhary@gmail.com Classification Model : SVM - Linear In Python, what is the class used to create a logistic regression classifier ? •
- 4. Data Science Prepared by Manish Email : Classification Model : SVM : Kernel In Python, what is the class used to create a logistic regression classifier ? • Linear Polynomial Gaussian (RBF) Sigmoid
- 5. Data Science Prepared by Manish Email : Classification Model : SVC - Kernel In Python, what is the class used to create a logistic regression classifier ? •
- 6. Data Science Prepared by Manish IT.mnchoudhary@gmail.com Classification Model : SVM - RBF In Python, what is the class used to create a logistic regression classifier ? •
- 7. Data Science Prepared by Manish IT.mnchoudhary@gmail.com Classification Model : SVM In Python, what is the class used to create a logistic regression classifier ? • How to tune Parameters of SVM? sklearn.svm.SVC(C=1.0, kernel='rbf', degree=3, gamma=0.0, coef0=0.0, shrinking=True, probability=False,tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, random_state=None) gamma: Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’. Higher the value of gamma, will try to exact fit the as per training data set i.e. generalization error and cause over-fitting problem. svc = svm.SVC(kernel='rbf', C=1,gamma=0).fit(X, y)
- 8. Data Science Prepared by Manish IT.mnchoudhary@gmail.com Pros & Cons: SVM In Python, what is the class used to create a logistic regression classifier ? • Advantage and Disadvantage associated with SVM Advantage: oIt works really well with clear margin of separation oIt is effective in high dimensional spaces. oIt is effective in cases where number of dimensions is greater than the number of samples. oIt uses a subset of training points in the decision function (called support vectors), so it is oalso memory efficient. Disadvantage: oIt doesn’t perform well, when we have large data set because the required training time is higher oIt also doesn’t perform very well, when the data set has more noise i.e. target classes are overla oSVM doesn’t directly provide probability estimates, these are calculated using an expensive ofive-fold cross-validation. It is related SVC method of Python scikit-learn library.