Which is better decision tree or Knn?

Both are non-parametric methods. Decision tree supports automatic feature interaction, whereas KNN cant. Decision tree is faster due to KNN’s expensive real time execution.

How do you determine if a dataset is linearly separable?

The recipe to check for linear separability is:

1. Instantiate a SVM with a big C hyperparameter (use sklearn for ease).
2. Train the model with your data.
3. Classify the train set with your newly trained SVM.
4. If you get 100\% accuracy on classification, congratulations! Your data is linearly separable.

What type of problems are best suited for decision tree learning?

Decision tree learning is generally best suited to problems with the following characteristics: Instances are represented by attribute-value pairs. There is a finite list of attributes (e.g. hair colour) and each instance stores a value for that attribute (e.g. blonde).

What kind of data is suitable for decision tree?

Decision trees are used for handling non-linear data sets effectively. The decision tree tool is used in real life in many areas, such as engineering, civil planning, law, and business. Decision trees can be divided into two types; categorical variable and continuous variable decision trees.

What is a linearly separable dataset?

A dataset is called linearly separable if its different clusters can be reliably separated by a linear hyperplane. We propose a measure of linear separability, easily computed as an angle that arises naturally in our analysis.

What happens if the examples are not linearly separable?

“Although the perceptron rule finds a successful weight vector when the training examples are linearly separable, it can fail to converge if the examples are not linearly separable. “

What is the issue with decision tree?

Disadvantages of decision trees: They are unstable, meaning that a small change in the data can lead to a large change in the structure of the optimal decision tree. They are often relatively inaccurate.

What is well defined learning problem?

Well Posed Learning Problem – A computer program is said to learn from experience E in context to some task T and some performance measure P, if its performance on T, as was measured by P, upgrades with experience E. Any problem can be segregated as well-posed learning problem if it has three traits – Attention reader!

Where do we use decision tree?

Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal, but are also a popular tool in machine learning.

What is the use of decision tree in machine learning?

Introduction Decision Trees are a type of Supervised Machine Learning (that is you explain what the input is and what the corresponding output is in the training data) where the data is continuously split according to a certain parameter. The tree can be explained by two entities, namely decision nodes and leaves.