11-04-2023, 12:07 AM
Decision Trees using R - Bank Loan Default Prediction
Learn Decision Trees using R with a case study to predict Bank Loan Default
New
Rating: 0.0 out of 5
(0 ratings)
661 students
1.5 hours on-demand video
Description
The decision tree is a key challenge in R and the strength of the tree is they are easy to understand and read when compared with other models. They are being popularly used in data science problems. These are the tool produces the hierarchy of decisions implemented in statistical analysis. Statistical knowledge is required to understand the logical interpretations of the Decision tree. As we have seen the decision tree is easy to understand and the results are efficient when it has fewer class labels and the other downside part of them is when there are more class labels calculations become complexed. This course makes one become proficient to build predictive and tree-based learning models.
Decision Tree in R is a machine-learning algorithm that can be a classification or regression tree analysis. The decision tree can be represented by graphical representation as a tree with leaves and branches structure. The leaves are generally the data points and branches are the condition to make decisions for the class of data set. Decision trees in R are considered as supervised Machine learning models as possible outcomes of the decision points are well defined for the data set. It is also known as the CART model or Classification and Regression Trees. There is a popular R package known as rpart which is used to create the decision trees in R.
https://www.udemy.com/course/decision-trees-using-r-bank-loan-default-prediction/?couponCode=EDUCBATARGETED1
Enjoy!
Learn Decision Trees using R with a case study to predict Bank Loan Default
New
Rating: 0.0 out of 5
(0 ratings)
661 students
1.5 hours on-demand video
Description
The decision tree is a key challenge in R and the strength of the tree is they are easy to understand and read when compared with other models. They are being popularly used in data science problems. These are the tool produces the hierarchy of decisions implemented in statistical analysis. Statistical knowledge is required to understand the logical interpretations of the Decision tree. As we have seen the decision tree is easy to understand and the results are efficient when it has fewer class labels and the other downside part of them is when there are more class labels calculations become complexed. This course makes one become proficient to build predictive and tree-based learning models.
Decision Tree in R is a machine-learning algorithm that can be a classification or regression tree analysis. The decision tree can be represented by graphical representation as a tree with leaves and branches structure. The leaves are generally the data points and branches are the condition to make decisions for the class of data set. Decision trees in R are considered as supervised Machine learning models as possible outcomes of the decision points are well defined for the data set. It is also known as the CART model or Classification and Regression Trees. There is a popular R package known as rpart which is used to create the decision trees in R.
https://www.udemy.com/course/decision-trees-using-r-bank-loan-default-prediction/?couponCode=EDUCBATARGETED1
Enjoy!