** Why is SVM good for classification **

Advantages of SVM Classifier:

SVM works relatively well when there is a clear margin of separation between classes. SVM is more effective in high dimensional spaces and is relatively memory efficient. SVM is effective in cases where the dimensions are greater than the number of samples.

** Why is SVM good for text classification **

SVM generalizes well in high dimensional spaces like those corresponding to texts. It is effective with more dimensions than samples. It works well when classes are well separated. SVM is a binary model in its conception, although it could be applied to classifying multiple classes with very good results.

** Why SVM is better than other classifiers **

Advantages. SVM Classifiers offer good accuracy and perform faster prediction compared to Naïve Bayes algorithm. They also use less memory because they use a subset of training points in the decision phase. SVM works well with a clear margin of separation and with high dimensional space.

** Is SVM better for classification or regression **

SVM tries to finds the “best” margin (distance between the line and the support vectors) that separates the classes and this reduces the risk of error on the data, while logistic regression does not, instead it can have different decision boundaries with different weights that are near the optimal point.

** Is SVM good for classification **

SVM is a supervised machine learning algorithm which can be used for classification or regression problems. It uses a technique called the kernel trick to transform your data and then based on these transformations it finds an optimal boundary between the possible outputs.

** Why does SVM give better accuracy **

It gives very good results in terms of accuracy when the data are linearly or non-linearly separable. When the data are linearly separable, the SVMs result is a separating hyperplane, which maximizes the margin of separation between classes, measured along a line perpendicular to the hyperplane.

** Why is SVM better than linear regression **

To sum up: Linear Regression has explicit decision and SVM finds approximate of real decision because of numerical(computational) solution.

** Why SVM is better than neural network **

We know that neural networks require significantly more time to train over a given dataset, with comparison to SVMs. Since, in this case, time is of the essence, our best bet is to use a support vector machine.

** How does SVM work for solving classification problem **

SVM works by mapping data to a high-dimensional feature space so that data points can be categorized, even when the data are not otherwise linearly separable. A separator between the categories is found, then the data are transformed in such a way that the separator could be drawn as a hyperplane.

** Why is SVM more accurate than logistic **

Unlike logistic regression, SVMs are designed to generate more complex decision boundaries. An LS-SVM with a simple linear kernel function corresponds to a linear decision boundary. Instead of a linear kernel, more complex kernel functions, such as the commonly used RBF kernel, can be chosen.

** What is the main advantage of SVM **

The advantages of SVM and support vector regression include that they can be used to avoid the difficulties of using linear functions in the high-dimensional feature space, and the optimization problem is transformed into dual convex quadratic programs.

** Is SVM good for multi class classification **

In its most simple type, SVM doesn't support multiclass classification natively. It supports binary classification and separating data points into two classes. For multiclass classification, the same principle is utilized after breaking down the multiclassification problem into multiple binary classification problems.

** Why is SVM more accurate than KNN **

KNN vs SVM :

SVM take cares of outliers better than KNN. If training data is much larger than no. of features(m>>n), KNN is better than SVM. SVM outperforms KNN when there are large features and lesser training data.

** Which algorithm is best for multi-class classification **

You can use decision tree techniques and logistic regression for multiclass classification. To handle this particular problem, you can use a machine learning algorithm for multiclass classification like Neural Networks, Naive Bayes, and SVM.

** How SVM can be used for classification problem **

However, it is mostly used in classification problems, such as text classification. In the SVM algorithm, we plot each data item as a point in n-dimensional space (where n is the number of features you have), with the value of each feature being the value of a particular coordinate.

** Why do we prefer SVM over neural networks **

We know that neural networks require significantly more time to train over a given dataset, with comparison to SVMs. Since, in this case, time is of the essence, our best bet is to use a support vector machine.

** Which classification algorithm is best and why **

Naive Bayes classifier algorithm gives the best type of results as desired compared to other algorithms like classification algorithms like Logistic Regression, Tree-Based Algorithms, Support Vector Machines. Hence it is preferred in applications like spam filters and sentiment analysis that involves text.

** How SVM can be used for multi-class classification **

In its most simple type, SVM doesn't support multiclass classification natively. It supports binary classification and separating data points into two classes. For multiclass classification, the same principle is utilized after breaking down the multiclassification problem into multiple binary classification problems.

** Is SVM a highly accurate classification method **

In SVM, the data is classified into two classes and the hyper plane lies between those two classes. The advantage of SVM is that it also considers data being close to the opposite class and thus gives a reliable classification.

** Which algorithm is best for classification problem **

Naive Bayes classifier algorithm gives the best type of results as desired compared to other algorithms like classification algorithms like Logistic Regression, Tree-Based Algorithms, Support Vector Machines.

** What are the best algorithms for classification **

Top 5 Classification Algorithms in Machine LearningLogistic Regression.Naive Bayes.K-Nearest Neighbors.Decision Tree.Support Vector Machines.

** Why is SVM used in binary classification **

You can use a support vector machine (SVM) when your data has exactly two classes. An SVM classifies data by finding the best hyperplane that separates all data points of one class from those of the other class. The best hyperplane for an SVM means the one with the largest margin between the two classes.

** Which method of classification is best **

In Biology, "Taxonomical classification" is the "best method of classification". Explanation: This is because, all living organisms are needed to be classified in groups, so as to find out their similarities and their differences.

** What is the fastest classification algorithm **

In terms of Runtime, the fastest algorithms are Naive Bayes, Support Vector Machine, Voting Classifier and the Neural Network.

** What is the difference between SVM and other classification algorithms **

SVMs are different from other classification algorithms because of the way they choose the decision boundary that maximizes the distance from the nearest data points of all the classes. The decision boundary created by SVMs is called the maximum margin classifier or the maximum margin hyper plane.