What are the advantages of different classification algorithms?

High performance on non – linear problems, not biased by outliers, not sensitive to overfitting. Not the best choice for large number of features, more complex. Efficient, not biased by outliers, works on non – linear problems, probabilistic approach.

What is the advantage of naive Bayesian over decision tree?

Naive bayes does quite well when the training data doesn’t contain all possibilities so it can be very good with low amounts of data. Decision trees work better with lots of data compared to Naive Bayes. Naive Bayes is used a lot in robotics and computer vision, and does quite well with those tasks.

What is the main disadvantages of classification of data?

Answer: A disadvantage to classification is that many of the classifications themselves are based on subjective judgments, which may or may not be shared by everyone participating.

What are the disadvantages of KNN algorithm?

Some Disadvantages of KNN

• Accuracy depends on the quality of the data.
• With large data, the prediction stage might be slow.
• Sensitive to the scale of the data and irrelevant features.
• Require high memory – need to store all of the training data.
• Given that it stores all of the training, it can be computationally expensive.

What is the main advantages of machine learning?

Advantages of Machine Learning Machine Learning algorithms are capable of learning from the data we provide. As new data is provided, the model’s accuracy and efficiency to make decisions improve with subsequent training.

What are the advantages and disadvantages of kNN algorithm in machine learning?

Lets find out some advantages and disadvantages of KNN algorithm. 1. No Training Period: KNN is called Lazy Learner (Instance based learning). It does not learn anything in the training period. It does not derive any discriminative function from the training data. In other words, there is no training period for it.

What are the disadvantages of decision tree in machine learning?

Decision Tree is used to solve both classification and regression problems. But the main drawback of Decision Tree is that it generally leads to overfitting of the data. Lets discuss its advantages and disadvantages in detail. 1.

What are the advantages of PCA in machine learning?

3. Reduces Overfitting: Overfitting mainly occurs when there are too many variables in the dataset. So, PCA helps in overcoming the overfitting issue by reducing the number of features. 4. Improves Visualization: It is very hard to visualize and understand the data in high dimensions.

What are the advantages of naive Bayes in machine learning?

Advantages of Naive Bayes. 1. When assumption of independent predictors holds true, a Naive Bayes classifier performs better as compared to other models. 2. Naive Bayes requires a small amount of training data to estimate the test data. So, the training period is less.