** Is SVM better than decision tree **

Decision Tree vs SVM stated that SVM uses a “kernel trick to solve non-linear problems whereas decision trees derive hyper-rectangles in input space to solve the problem” and “decision trees are better for categorical data and it deals with collinearity” better than SVM.

** What is one advantage of support vector machine SVM over decision tree models **

One of the main advantages of SVM is that it works well in high-dimensional spaces and it's relatively memory efficient. it also able to handle non-linearly separable data by transforming them into a higher dimensional space where they become linear separable, this is done by using kernel trick.

** Which model is better than SVM **

If given as much training and computational power as possible, however, NNs tend to outperform SVMs. As we'll see in the next section, though, the time required to train the two algorithms is vastly different for the same dataset.

** 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.

** What is better than decision tree **

Random forest algorithm avoids and prevents overfitting by using multiple trees. The results are not accurate. This gives accurate and precise results. Decision trees require low computation, thus reducing time to implement and carrying low accuracy.

** Why is SVM the best model **

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 algorithm best **

What makes the linear SVM algorithm better than some of the other algorithms, like k-nearest neighbors, is that it chooses the best line to classify your data points. It chooses the line that separates the data and is the furthest away from the closet data points as possible.

** Why decision tree is the best algorithm **

Advantages of the Decision Tree

It can be very useful for solving decision-related problems. It helps to think about all the possible outcomes for a problem. There is less requirement of data cleaning compared to other algorithms.

** Why is decision tree the best model **

In a Decision Tree, it is effortless to trace each path to a conclusion. It ensures a comprehensive analysis of the consequences of each branch while also recognizing which nodes might need further analyzing. Therefore, it is easy to validate the algorithm using statistical tests.

** Why is SVM more accurate **

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.

** 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.

** What is the advantage of decision tree over SVM **

Decision tree vs SVM :

SVM uses kernel trick to solve non-linear problems whereas decision trees derive hyper-rectangles in input space to solve the problem. Decision trees are better for categorical data and it deals colinearity better than SVM.

** Why is SVM less for overfitting **

In SVM, to avoid overfitting, we choose a Soft Margin, instead of a Hard one i.e. we let some data points enter our margin intentionally (but we still penalize it) so that our classifier don't overfit on our training sample. Here comes an important parameter Gamma (γ), which control Overfitting in SVM.

** 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.

** Which algorithm is best for decision tree **

The best algorithm for decision trees depends on the specific problem and dataset. Popular decision tree algorithms include ID3, C4. 5, CART, and Random Forest. Random Forest is considered one of the best algorithms as it combines multiple decision trees to improve accuracy and reduce overfitting.

** Why decision tree is better in machine learning **

Decision trees in machine learning provide an effective method for making decisions because they lay out the problem and all the possible outcomes. It enables developers to analyze the possible consequences of a decision, and as an algorithm accesses more data, it can predict outcomes for future data.

** Why decision tree is better than other algorithms **

As one can see, trees are an easy and convenient way to visualize the results of algorithms and understand how decisions are made. The main advantage of a decision tree is that it adapts quickly to the dataset. The final model can be viewed and interpreted in an orderly manner using a "tree" diagram.

** 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.

** What is SVM more suitable for **

SVM is more effective in high dimensional spaces. SVM is effective in cases where the number of dimensions is greater than the number of samples.

** Why decision trees are better **

Decision trees are extremely useful for data analytics and machine learning because they break down complex data into more manageable parts. They're often used in these fields for prediction analysis, data classification, and regression.

** Which is the best classifier algorithm **

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.

** Why is decision tree a good algorithm **

It ensures a comprehensive analysis of the consequences of each branch while also recognizing which nodes might need further analyzing. Therefore, it is easy to validate the algorithm using statistical tests. This makes Decision Trees an accountable model.

** Why SVM is not good for large datasets **

Abstract. Support vector machine (SVM) is a powerful technique for data classification. Despite of its good theoretic foundations and high classification accuracy, normal SVM is not suitable for classification of large data sets, because the training complexity of SVM is highly dependent on the size of data set.

** Why are decision trees the most popular classification technique **

Decisions trees are popular in machine learning as they are a simple way of structuring a model. The tree-like structure also makes it simple to understand the decision-making process of the model.

** What is the best classifier to use **

Our chosen ML algorithms for classification are:Logistic Regression.Naive Bayes.K-Nearest Neighbors.Decision Tree.Support Vector Machines.