## How do we conduct cost-sensitive classification in Weka?

A meta classifier that makes its base classifier cost-sensitive. Two methods can be used to introduce cost-sensitivity: reweighting training instances according to the total cost assigned to each class; or predicting the class with minimum expected misclassification cost (rather than the most likely class).

### Is decision tree sensitive to imbalanced dataset?

The decision tree algorithm is effective for balanced classification, although it does not perform well on imbalanced datasets. The split points of the tree are chosen to best separate examples into two groups with minimum mixing.

#### What is cost-sensitive algorithm?

Cost-sensitive learning is a subfield of machine learning that involves explicitly defining and using costs when training machine learning algorithms. Cost-sensitive techniques may be divided into three groups, including data resampling, algorithm modifications, and ensemble methods.

**How is decision tree used in Weka tool?**

Open Weka GUI. Select the “Explorer” option. Select “Open file” and choose your dataset….Classification using Decision Tree in Weka

- Click on the “Classify” tab on the top.
- Click the “Choose” button.
- From the drop-down list, select “trees” which will open all the tree algorithms.
- Finally, select the “RepTree” decision tree.

**Is logistic regression sensitive to imbalanced data?**

Logistic regression does not support imbalanced classification directly. Instead, the training algorithm used to fit the logistic regression model must be modified to take the skewed distribution into account.

## Is decision tree sensitive to outliers?

Decision trees are also not sensitive to outliers since the partitioning happens based on the proportion of samples within the split ranges and not on absolute values.

### What is the difference between decision tree and random forest?

The critical difference between the random forest algorithm and decision tree is that decision trees are graphs that illustrate all possible outcomes of a decision using a branching approach. In contrast, the random forest algorithm output are a set of decision trees that work according to the output.

#### What is cost sensitive index?

changes of a certain cost outcome against percentage. variations from a base estimate of a certain risk variable, say, Xi. Such a relationship can be expressed in terms of. a cost ‘sensitivity index’.

**What is cost sensitive SVM?**

This modification of SVM that weighs the margin proportional to the class importance is often referred to as weighted SVM, or cost-sensitive SVM.

**What is SMO in WEKA?**

SMO refers to the specific efficient optimization algorithm used inside the SVM implementation, which stands for Sequential Minimal Optimization. Weka Configuration for the Support Vector Machines Algorithm.

## How is accuracy calculated in WEKA?

The total number of correctly instances divided by total number of instances gives the accuracy. In weka, % of correctly classified instances give the accuracy of the model.

### Do you need balanced data for logistic regression?

Logistic regression requires dependent variable which is in binary form i.e., 0 and 1. A balanced sample means if you have thirty 0, you also need thirty 1. But, there is no such condition in logistic regression.

#### How does logistic regression deal with imbalanced data?

In logistic regression, another technique comes handy to work with imbalance distribution. This is to use class-weights in accordance with the class distribution. Class-weights is the extent to which the algorithm is punished for any wrong prediction of that class.

**Is smote better than undersampling?**

The authors of the technique recommend using SMOTE on the minority class, followed by an undersampling technique on the majority class. The combination of SMOTE and under-sampling performs better than plain under-sampling. — SMOTE: Synthetic Minority Over-sampling Technique, 2011.

**What is difference between oversampling and smote?**

What is the difference between these two techniques? Undersampling would decrease the proportion of your majority class until the number is similar to the minority class. At the same time, Oversampling would resample the minority class proportion following the majority class proportion.

## Is decision tree sensitive to missing values?

Decision Tree can automatically handle missing values. Decision Tree is usually robust to outliers and can handle them automatically.

### Is decision tree sensitive to noisy data?

A decision tree is sensitive (or insensitive) to noises in a test data set depending on which attributes are noisy. A decision tree makes use of a small subset of attributes for classification.

#### Is random forest more efficient 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.

**Which is more stable random forest or decision tree?**

Random forests consist of multiple single trees each based on a random sample of the training data. They are typically more accurate than single decision trees. The following figure shows the decision boundary becomes more accurate and stable as more trees are added.

**How do you calculate price sensitivity?**

Price sensitivity can be measured by dividing the percentage change in quantity demanded by the percentage change in price.

## Which are different types of cost indexes?

Cost Indexes for different locations

- Cost and availability of materials.
- Cost and availability of labor.
- Cost of transportation of equipment and labor.
- Import duties and local taxes.
- Currency exchange rates.

### Does SVM work with imbalanced data?

The Support Vector Machine algorithm is effective for balanced classification, although it does not perform well on imbalanced datasets. The SVM algorithm finds a hyperplane decision boundary that best splits the examples into two classes.

#### Should I normalize data for SVM?

Prescaling/normalization/whitening SVMs assume that the data it works with is in a standard range, usually either 0 to 1, or -1 to 1 (roughly). So the normalization of feature vectors prior to feeding them to the SVM is very important.

**What is SVM in WEKA?**

A key parameter in SVM is the type of Kernel to use. The simplest kernel is a Linear kernel that separates data with a straight line or hyperplane. The default in Weka is a Polynomial Kernel that will separate the classes using a curved or wiggly line, the higher the polynomial, the more wiggly (the exponent value).