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Decision trees are well-suited for supervised learning tasks, where the goal is to predict an output variable based on input features. The tree structure allows for easy interpretation of how the input variables are related to the output, making it ideal for classification and regression tasks. K-means Clustering: A clustering technique used in unsupervised learning. PCA: A dimensionality reduction technique, not a supervised learning algorithm. k-Nearest Neighbors (k-NN): While k-NN can be used for supervised learning, decision trees are generally more interpretable. Support Vector Machines (SVM): While SVMs are also used in supervised learning, decision trees offer more visual interpretability.
What is the major difference between Pradhan Mantri Jeevan Jyoti Bima Yojana (PMJJBY) & Pradhan Mantri Suraksha Bima Yojana (PMSBY)?
Which currencies were included in LIBOR?
Net Interest Margin is a key profitability metric for banks. How is it best described?
What does the capability approach focus on in achieving justice?
As per RBI guidelines, for the purpose of calculation of LTV in case of housing loans, stamp duty, registration and other documentation charges can be a...
The ownership structure of a Regional Rural bank is?
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Consider the following statements
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2. The ...
What is the provisioning requirement for a standard asset for fund based facilities of Farm Credit to agricultural activities, individual housing loans ...
Which of the following is not a feature of debentures?