| Classification Algorithms | Accuracy | F1-Score |
|---|---|---|
| Naïve Bayes | 80.11% | 0.6005 |
| Stochastic Gradient Descent | 82.20% | 0.5780 |
| K-Nearest Neighbours | 83.56% | 0.5924 |
| Decision Tree | 84.23% | 0.6308 |
Also to know is, which is the best classification algorithm in machine learning?
Top 10 Machine Learning Algorithms
- Naive Bayes Classifier Algorithm.
- K Means Clustering Algorithm.
- Support Vector Machine Algorithm.
- Apriori Algorithm.
- Linear Regression.
- Logistic Regression.
- Decision Tree.
- Random Forest.
Beside above, how do you classify the accuracy of an algorithm? Classification Accuracy
It is the ratio of number of correct predictions to the total number of input samples. It works well only if there are equal number of samples belonging to each class.
Besides, how does classification algorithm work?
Classifier: An algorithm that maps the input data to a specific category. Classification model: A classification model tries to draw some conclusions from the input values given for training. It will predict the class labels/categories for the new data.
Can SVM do multiclass classification?
Multiclass Classification using Support Vector Machine
In its most simple type SVM are applied on binary classification, dividing data points either in 1 or 0. For multiclass classification, the same principle is utilized. It basically divides the data points in class x and rest.
