What is the difference between a regressor and a classifier
Regression algorithms solve regression problems such as house price predictions and weather predictions. Classification algorithms solve classification problems like identifying spam e-mails, spotting cancer cells, and speech recognition.
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.
What is classifier and regression
Classification is the task of predicting a discrete class label. Regression is the task of predicting a continuous quantity.
What is the difference between classification and regression in neural networks
Regression refers to predictive modeling problems that involve predicting a numeric value given an input. Classification refers to predictive modeling problems that involve predicting a class label or probability of class labels for a given input.
What is the difference between random forest classifier and regressor
A random forest classifier works with data having discrete labels or better known as class. Example- A patient is suffering from cancer or not, a person is eligible for a loan or not, etc. A random forest regressor works with data having a numeric or continuous output and they cannot be defined by classes.
Is Naive Bayes classifier or Regressor
The Naive Bayes algorithm is a classification algorithm based on Bayes rule and a set of conditional independence assumptions.
Is logistic regression a regressor or a classifier
Logistic regression is a classification technique borrowed by machine learning from the field of statistics. Logistic Regression is a statistical method for analyzing a dataset in which there are one or more independent variables that determine an outcome.
What is random forest regressor vs classifier
A random forest classifier works with data having discrete labels or better known as class. Example- A patient is suffering from cancer or not, a person is eligible for a loan or not, etc. A random forest regressor works with data having a numeric or continuous output and they cannot be defined by classes.
What is the difference between classification and regression and clustering
Clustering is an example of an unsupervised learning algorithm, in contrast to regression and classification, which are both examples of supervised learning algorithms. Data may be labeled via the process of classification, while instances of similar data can be grouped together through the process of clustering.
How is regression different from classification and clustering
Regression and Classification are types of supervised learning algorithms while Clustering is a type of unsupervised algorithm. When the output variable is continuous, then it is a regression problem whereas when it contains discrete values, it is a classification problem.
What is the difference between decision tree classifier and regressor in Python
The fundamental difference is that for classification, splits are based on Gini impurity error calculations whereas for regression, Mean Squared Error minimization is used. Tree traversal based on information gain and evaluation works pretty much the same way for both tasks.
Is random forest classifier a regression model
Random Forest is an ensemble technique capable of performing both regression and classification tasks with the use of multiple decision trees and a technique called Bootstrap and Aggregation, commonly known as bagging.
What is the difference between Naive Bayes classifier and logistic regression
Logistic Regression vs Naive Bayes :
Naive bayes is a generative model whereas LR is a discriminative model. Naive bayes works well with small datasets, whereas LR+regularization can achieve similar performance. LR performs better than naive bayes upon colinearity, as naive bayes expects all features to be independent.
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
Classification is about predicting a label, by identifying which category an object belongs to based on different parameters. Regression is about predicting a continuous output, by finding the correlations between dependent and independent variables.
What is the difference between classifier and clustering
Classification is a supervised learning approach where a specific label is provided to the machine to classify new observations. Here the machine needs proper testing and training for the label verification. Clustering is an unsupervised learning approach where grouping is done on similarities basis.
What is the difference between classification and regression Quora
Regression is used to predict continuous values. Classification is used to predict which class a data point is part of (discrete value). Example: I have a house with W rooms, X bathrooms, Y square-footage and Z lot-size.
What is classification vs regression towards data science
Regression tasks are concerned with predicting a continuous value, whereas classification tasks are concerned with predicting discrete values.
What is the difference between classification and regression in Python
Classification gives out discrete values. Regression gives continuous values. Given a group of data, this method helps group the data into different groups.
What is the difference between KNN classifier and regressor
KNN regression tries to predict the value of the output variable by using a local average. KNN classification attempts to predict the class to which the output variable belong by computing the local probability.
What is the difference between a random forest classifier and a regressor
A random forest classifier works with data having discrete labels or better known as class. Example- A patient is suffering from cancer or not, a person is eligible for a loan or not, etc. A random forest regressor works with data having a numeric or continuous output and they cannot be defined by classes.
What is the difference between classification and regression random forest
In a random forest regression, each tree produces a specific prediction. The mean prediction of the individual trees is the output of the regression. This is contrary to random forest classification, whose output is determined by the mode of the decision trees' class.
What is the difference between linear classifier and logistic regression
The Differences Between Linear Regression and Logistic Regression. Linear Regression is used to handle regression problems whereas Logistic regression is used to handle the classification problems. Linear regression provides a continuous output but Logistic regression provides discreet output.
What are two 2 differences between classification and clustering
Differences between Classification and Clustering
Classification is more complex as compared to clustering as there are many levels in the classification phase whereas only grouping is done in clustering. Classification examples are Logistic regression, Naive Bayes classifier, Support vector machines, etc.
What is the difference between classification and regression when using SVM
For SVM classification the hinge loss is used, for SVM regression the epsilon insensitive loss function is used.
