It depends on the dataset you have. You may check logistic regression, and SVM to see if they fit.

Machine Learning (ML) techniques are diverse and are used for various tasks such as prediction, classification, and data analysis. Here are some of the key ML techniques:

1.Regression: This technique is used to predict numerical values based on previous data. It's often used in forecasting and finding out the relationship between variables.

2. Classification: This method involves categorizing data into predefined classes. It's commonly used in applications like spam detection or image recognition.

3. Clustering: Clustering algorithms are used to group a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups.

4. Anomaly Detection: This technique is used to identify unusual patterns that do not conform to expected behavior. It's widely used in fraud detection.

5. Neural Networks: A sub-field of ML, neural networks are used for learning and making decisions. Deep learning, a subset of neural networks, is particularly powerful for handling large amounts of unstructured data.

6. Decision Trees: These are used for making predictions or decisions by learning simple decision rules inferred from prior data.

7. Support Vector Machines (SVM): SVMs are used for classification and regression tasks. They work well for smaller datasets and can model non-linear decision boundaries.

8. Ensemble Methods: This technique combines the predictions of several base estimators to improve generalizability and robustness over a single estimator.

9. Dimensionality Reduction: Techniques like PCA are used to reduce the number of random variables to consider, by obtaining a set of principal variables.

10. Reinforcement Learning: This area of ML is concerned with how software agents ought to take actions in an environment to maximize some notion of cumulative reward.

Each of these techniques can be applied to a wide range of problems, and the choice of technique often depends on the nature of the data and the specific requirements of the task.

Hello, maybe this reference can support you to answer your question: Article Hybrid approaches to optimization and machine learning metho...