What is the difference among XGBoost LightGBM and CatBoost
In CatBoost, symmetric trees, or balanced trees, refer to the splitting condition being consistent across all nodes at the same depth of the tree. LightGBM and XGBoost, on the other hand, results in asymmetric trees, meaning splitting condition for each node across the same depth can differ.
What is a gradient boosting model
Gradient Boosting is a powerful boosting algorithm that combines several weak learners into strong learners, in which each new model is trained to minimize the loss function such as mean squared error or cross-entropy of the previous model using gradient descent.
Is XGBoost a classifier or regression
What is XGBoost XGBoost, which stands for Extreme Gradient Boosting, is a scalable, distributed gradient-boosted decision tree (GBDT) machine learning library. It provides parallel tree boosting and is the leading machine learning library for regression, classification, and ranking problems.
Is XGBoost better than logistic regression
This study aims to predict the potential of customers in taking credit facilities or becoming a debtor. The study is conducted by comparing logistic regression model and XGBoost. The accuracy of the testing data on the logistic regression model is 88% while the XGBoost is 92%.
Is gradient boosting regression or classification
Gradient Boosting is a tree-based algorithm, which sits under the supervised branch of Machine Learning. Note that it can be used for both classification and regression problems.
What is the difference between gradient boosting classifier and regressor
When the target column is continuous, we use Gradient Boosting Regressor whereas when it is a classification problem, we use Gradient Boosting Classifier. The only difference between the two is the “Loss function”. The objective here is to minimize this loss function by adding weak learners using gradient descent.
Is logistic regression a classifier
Logistic Regression is a classification technique used in machine learning. It uses a logistic function to model the dependent variable. The dependent variable is dichotomous in nature, i.e. there could only be two possible classes (eg.: either the cancer is malignant or not).
Are logistic regression models classifiers
Logistic regression is a simple yet very effective classification algorithm so it is commonly used for many binary classification tasks.
Is SVM better than logistic regression
Logistic regression and SVM with a linear kernel have similar performance but depending on your features, one may be more efficient than the other. Logistic regression and SVM are great tools for training classification and regression problems.
How is gradient boosting classifier different from logistic regression
Fundamentally, Gradient Boosting is an induction-based approach for classification and prediction. In contrast, Logistic Regression is a linearly (or curvy linearly) analysis approach that uses a generalized linear equation to describe the directed dependencies among a set of variables.
What is the difference between classifier and regressor
The key distinction between Classification vs Regression algorithms is Regression algorithms are used to determine continuous values such as price, income, age, etc. and Classification algorithms are used to forecast or classify the distinct values such as Real or False, Male or Female, Spam or Not Spam, etc.
What is the difference between classifier and regressor in machine learning
The most significant difference between regression vs classification is that while regression helps predict a continuous quantity, classification predicts discrete class labels. There are also some overlaps between the two types of machine learning algorithms.
Why is logistic regression called classifier
Logistic Regression is one of the basic and popular algorithms to solve a classification problem. It is named 'Logistic Regression' because its underlying technique is quite the same as Linear Regression. The term “Logistic” is taken from the Logit function that is used in this method of classification.
Why is logistic regression not classification
It is only a classification algorithm in combination with a decision rule that makes dichotomous the predicted probabilities of the outcome. Logistic regression is a regression model because it estimates the probability of class membership as a (transformation of a) multilinear function of the features.
What is a better classifier than logistic regression
As mentioned above, tree-based methods tend to outperform LR when it comes to classification. However, SVMs are gaining popularity as an alternative. SVMs combine computer algorithms and theoretical results, which has resulted in a good reputation for classification purposes.
What is the difference between SVM and logistic regression for classification
SVM tries to finds the “best” margin (distance between the line and the support vectors) that separates the classes and this reduces the risk of error on the data, while logistic regression does not, instead it can have different decision boundaries with different weights that are near the optimal point.
What is the difference between classifier and logistic regression
The main difference between Regression and Classification algorithms that Regression algorithms are used to predict the continuous values such as price, salary, age, etc. and Classification algorithms are used to predict/Classify the discrete values such as Male or Female, True or False, Spam or Not Spam, etc.
Why logistic regression is called as regression instead of classifier
Linear regression gives a continuous value of output y for a given input X. Whereas, logistic regression gives a continuous value of P(Y=1) for a given input X, which is later converted to Y=0 or Y=1 based on a threshold value. That's the reason, logistic regression has “Regression” in its name.
What is the difference between logistic regression and classifier
A classification algorithm may predict a continuous value, but the continuous value is in the form of a probability for a class label. A regression algorithm may predict a discrete value, but the discrete value in the form of an integer quantity.
Is logistic regression a classification algorithm
Logistic regression is a supervised learning classification algorithm used to predict the probability of a target variable. The nature of target or dependent variable is dichotomous, which means there would be only two possible classes.
Can logistic regression be used as a classifier
Logistic Regression is a statistical approach and a Machine Learning algorithm that is used for classification problems and is based on the concept of probability. It is used when the dependent variable (target) is categorical.
Why is logistic regression called regression if it is a classification algorithm
Logistic Regression is one of the basic and popular algorithms to solve a classification problem. It is named 'Logistic Regression' because its underlying technique is quite the same as Linear Regression. The term “Logistic” is taken from the Logit function that is used in this method of classification.
Is logistic regression a generative or a descriptive classifier
Logistic regression, SVM, and tree based classifiers (e.g. decision tree) are examples of discriminative classifiers. A discriminative model directly learns the conditional probability distribution P(y|x).
Is logistic regression a generative or a descriptive classifier Why
Naive bayes is a Generative model whereas Logistic Regression is a Discriminative model . Generative model is based on the joint probability, p( x, y), of the inputs x and the label y, and make their predictions by using Bayes rules to calculate p(y | x), and then picking the most likely label y.
Is logistic regression a generative classifier
These two at first seem to be similar , but these are fundamentally different models . Naive bayes is a Generative model whereas Logistic Regression is a Discriminative model .
