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# classifier or regressor ## python - how to check if sklearn model is classifier or regressor - stack overflow

By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. ## difference between classification and regression in machine learning

Predictive modeling can be described as the mathematical problem of approximating a mapping function (f) from input variables (X) to output variables (y). This is called the problem of function approximation.

It is common for classification models to predict a continuous value as the probability of a given example belonging to each output class. The probabilities can be interpreted as the likelihood or confidence of a given example belonging to each class. A predicted probability can be converted into a class value by selecting the class label that has the highest probability.

For example, a specific email of text may be assigned the probabilities of 0.1 as being spam and 0.9 as being not spam. We can convert these probabilities to a class label by selecting the not spam label as it has the highest predicted likelihood.

For example, if a classification predictive model made 5 predictions and 3 of them were correct and 2 of them were incorrect, then the classification accuracy of the model based on just these predictions would be:

Some algorithms have the word regression in their name, such as linear regression and logistic regression, which can make things confusing because linear regression is a regression algorithm whereas logistic regression is a classification algorithm.

Some algorithms can be used for both classification and regression with small modifications, such as decision trees and artificial neural networks. Some algorithms cannot, or cannot easily be used for both problem types, such as linear regression for regression predictive modeling and logistic regression for classification predictive modeling.

If the class labels in the classification problem do not have a natural ordinal relationship, the conversion from classification to regression may result in surprising or poor performance as the model may learn a false or non-existent mapping from inputs to the continuous output range.

Some algorithms have the word regression in their name, such as linear regression and logistic regression, which can make things confusing because linear regression is a regression algorithm whereas logistic regression is a classification algorithm

Very basic and insightful. I experience same misunderstanding from people when hiring talent and sometimes explaining these concepts to people. Will take a cue from your explanation going forward. Thanks !!!!

How is it possible to reverse the process of discretizing data given that there is a loss of information? For example, following the same example above values of \$0 to \$49 would be represented by categorical value of 0.

I am a patent attorney, used to be a physics animal. I have written several hundred patent applications directed to software, mostly cyber security and cloud security system patents. Lately, like in every field, AI and ML in particular is everywhere. I am not afraid of the math, but the vocabulary was an issue until I read your post.

I cant tell you how much I appreciated that. As you know, vocabulary, at least consistent vocabulary usage, is a bit of an issue in the data science and software worlds. Your post cleared up many questions in just a few minutes of reading THANK YOU!

Thank you for your great tutorials. Could you please give us a tutorial on how to classify images using transfer learning and Tensorflow? Or guide me where I can find great tutorial about it like yours?

I understand that short term price movements are a random walk and that a persistence model will be the best that you can achieve: https://machinelearningmastery.com/gentle-introduction-random-walk-times-series-forecasting-python/

Fantastic post, thank you for sharing! I was recently training a model as a binary classification problem using sigmoid as a single output. However, I was able to get far better results using MSE rather than binary cross-entropy. Since MSE is mostly used for regression, does this mean I was forced to convert it to a regression problem? This has been tickling my brain for quite some time now.

Excellent post! I appreciate too much your work Ph.D. Jason Brownlee. I have a question. What type of task should I perform if my dependent variable observations are dichotomous, but I need to infer continuous values?

Thank you very much for your help! I have used two variants to determine my continuous output: 1. The probability of belonging to the positive class in a classification model. 2. The output of a regression model. The accuracy in terms of a CMC curve for the classification model outperformed the regression model. But I am not sure if I have misunderstood some results. Is it feasible to assume the probability of belonging to the positive class in a classification model as the similarity to this class? When the classifier outputs the probability (p) to belong to the negative class, I computed the probability to belong to the positive class as 1-p.

What happens when a problem has many ordinal categories? For example, if I wanted to predict, I dont know, the goals (soccer) a team scores in a match (which generally will be in the range of 0 to 10) is a classification problem or a regression problem?

One thing I need to confirm, if I use movielens dataset to predict the rating, the regression prediction is the correct one, right? What if I use classification prediction in this case? May I got wrong prediction?

Are there any particular scenario that you can mention like a question which asks to implement Linear Regression and another question which ask to implement logistic regression (classification) so that we can implement in R.

In the case you can convert your model from Regression to Classification, as it is explained in your last section, which could be more convenient to apply Regression (continuos) or Classification (discrete labels e.g. you divided your continuos output in some segment of interest and associated them to some artificial range such very low, low, medium, high, super, ultra, super ultra, tremendously, such it is the current case for frequency spectrum :-)))Which model performs better Regression or Classification? using the same common layers (with the exception at the output layer and activation function?).

Hi Jason. I was hoping if you could help clarify. You say to use regression for predicting quantities and classification for labels. If I was interested in the predicting the probability of something being in a specific class, would it still be a classification problem?

Hi Jason, Thanks for the very informative blog. I do have a question. i want to predict Y, which can take the values 0,1or 2 based on an independent variable (X) which takes a value from 0 to 9. However, there are additional 8 categorical variables that are my control variables. I want to know if X predicts Y. I performed a ordinal logistic regression, however, I was told classification would be a better approach. My question is, how does one perform classification if control variables are to be taken into account too?

is it possible to generalize the amount of data that is needed for a regression and classification Problem? In my Opinion, to make a valid answer of how much something is, it needs more Data to learn from than just saying it is 1 or 0. Or is there no difference in the needed amount of Data for Regression/Classification Prolems? Thanks for an answer!

Thank you for that great article. Back to how you start: How do I calculate accuracy for my regression problem? I am still not sure why this isnt a valid question to ask. If your regression model predicts an amount between \$0 and \$100, we could still use various metrics to assess the accuracy of the prediction (average % error, max % error, error variance, etc.) No?

This explanation is just too awesome man.I had already studied about these concepts in depth a month ago.But most of the concepts used to appear so difficult to me until I read ur post.I am about to start a ml project in a couple of days,and I cant express how nicely ur post has cleared my concepts.Thank you so much

what should i do if want to make prediction that has output in discrete? for example i want to make prediction of the sum of product , and the input is behaviour of customer . can i predict using regression?

Hi Jason, I am still confusing after reading here. How to distinguish between classification and regression method? My response may be categorical, but my purpose is estimating the probability of each categorical. Is this still a classification problem? Is the response type decided what the method is instead of the type of purpose? I am so confused.

I hear people say how accurate is the model in reference to a regression problem all the time. This is how people were accustomed to saying it in grade school. How should I correct them? Instead of saying how accurate is the model what should we say?

1) I choose AirBnb data and want to predict the price for a given criteria by user. I mean i build a model and give my example data to predict price and it will give me a certain price. This is called Regression right?

The skill of the model is determined by comparing it to a naive method relative, you can learn more here: https://machinelearningmastery.com/faq/single-faq/how-to-know-if-a-model-has-good-performance

Thank you so much for your articles It makes me more understand than before. By the way, I have a small question related to the phrases you wrote as i am a new learner for machine learning. The phrase: A problem with more than two classes is often called a multi-class classification problem. A problem where an example is assigned multiple classes is called a multi-label classification problem. I misunderstand between these two phrases. Can you give me small examples btw these two. Thank you much.

Is it possible to decompose a problem with a combination of regression + classification? For example, a dataset has two measures, measure 1 is continuous, measure 2 is binary. Using regression for measures, and classification for measure 2?

Classification and regression refer to the target that is being predict, and typically it is one variable. You may have one or more input variables that are labels or numbers, but it not not impact whether the prediction problem is regression or classification.

What is the difference between linear regression and logistic regression? Is it correct that logistic regression uses the exact same formula as linear regression after it has raised all training data to an exponent and then normalized all the training data?

I have a concern regarding explainable artificial intelligence (XAI). Based on literature, XAI is usually applied to classification algorithms, not regression algorithms. Is there a convincing reason for that?

My requirement is to train my model with a NEW discrete class label incrementally (partial_fit) but without prior knowledge of NEW class in the first partial_fit training as it happened in SGD classifier with partial_fit.

Everything posted made a ton of sense. However, consider this, what if you were to create a awesome headline? I am not saying your information is not good., but what if you added something that grabbed folks attention? I mean Difference Between Classification and Regression in Machine Learning is a little boring. You could glance at Yahoos front page and see how they create post headlines to grab viewers interested. You might try adding a video or a picture or two to grab people excited about what youve written. Just my opinion, it might make your posts a little livelier.

Do all these posts are summarized anywhere in the form of a table of contents or in form of the mindmap. I want to connect all these posts in a sequence. It will help us to understand ML better i feel than randomly going to each post.

I once came here for understanding classification vs regression. I think I should understand these concepts since Simple Logistic in WEKA is the best classifier among others for my classification problem.

However, seem this classifier is actually using a logistic regression behind it, Im becoming confusing by the word regression part of its name. Thinking and finding answer either I am doing correct actions to determine the best classifier of mine. ## regression vs classification in machine learning: what is the difference? | springboard blog

Comparing regression vs classification in machine learning can sometimes confuse even the most seasoned data scientists. This can eventually make it difficult for them to implement the right methodologies for solving prediction problems. Both regression and classification are types of supervised machine learning algorithms, where a model is trained according to the existing model along with correctly labelled data. But there are also many differences between regression and classification algorithms that you should know in order to implement them correctly and sharpen your machine learning skills. In this blog, we will understand the difference between regression and classification algorithms.

Regression algorithms predict a continuous value based on the input variables. The main goal of regression problems is to estimate a mapping function based on the input and output variables. If your target variable is a quantity like income, scores, height or weight, or the probability of a binary category (like the probability of rain in particular regions), then you should use the regression model. The different types of regression algorithms include:

Classification is a predictive model that approximates a mapping function from input variables to identify discrete output variables, that can be labels or categories. The mapping function of classification algorithms is responsible for predicting the label or category of the given input variables.A classification algorithm can have both discrete and real-valued variables, but it requires that the examples be classified into one of two or more classes.

In this algorithm, a classification model is created by building a decision tree where every node of the tree is a test case for an attribute and each branch coming from the node is a possible value for that attribute.

This tree-based algorithm includes a set of decision trees which are randomly selected from a subset of the main training set. The random forest classification algorithm aggregates outputs from all the different decision trees to decide on the final output prediction, which is more accurate than any of the individual trees.

The K-nearest neighbor algorithm assumes that similar things exist in close proximity to each other. It uses feature similarity for predicting values of new data points. The algorithm helps grouping similar data points together according to their proximity. The main goal of the algorithm is to determine how likely it is for a data point to be a part of the specific group.

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.

Lets consider a dataset that contains student information of a particular university. A regression algorithm can be used in this case to predict the height of any student based on their weight, gender, diet, or subject major. We use regression in this case because height is a continuous quantity. There is an infinite number of possible values for a persons height.

On the contrary, classification can be used to analyse whether an email is a spam or not spam. The algorithm checks the keywords in an email and the senders address is to find out the probability of the email being spam. Similarly, while a regression model can be used to predict temperature for the next day, we can use a classification algorithm to determine whether it will be cold or hot according to the given temperature values.

Understanding the difference between regression and classification algorithms can help you apply machine learning concepts in a more accurate manner. Some algorithms may need both classification and regression approach, which is why an in-depth knowledge of both is crucial. For working professionals who want to pivot towards a successful machine learning career, Springboard offers machine learning career track program that comes with a 1:1 mentorship, project-driven approach along with career coaching. The course includes over 14 real-world projects and guarantees you a machine learning job. Take a look at the course and apply today.

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Here we have imported various modules like datasets, GradientBoostingRegressor, GradientBoostingClassifier and test_train_split from differnt libraries. We will understand the use of these later while using it in the in the code snipet. For now just have a look on these imports.

Here we have used datasets to load the inbuilt cancer dataset and we have created objects X and y to store the data and the target value respectively. dataset = datasets.load_breast_cancer() X = dataset.data y = dataset.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)

Here, we are using GradientBoostingClassifier as a Machine Learning model to fit the data. model = GradientBoostingClassifier() model.fit(X_train, y_train) print(model) Now we have predicted the output by passing X_test and also stored real target in expected_y. expected_y = y_test predicted_y = model.predict(X_test) Here we have printed classification report and confusion matrix for the classifier. print(metrics.classification_report(expected_y, predicted_y)) print(metrics.confusion_matrix(expected_y, predicted_y))

Here we have used datasets to load the inbuilt boston dataset and we have created objects X and y to store the data and the target value respectively. dataset = datasets.load_boston() X = dataset.data y = dataset.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)

Here, we are using GradientBoostingRegressor as a Machine Learning model to fit the data. model = GradientBoostingRegressor() model.fit(X_train, y_train) print(); print(model) Now we have predicted the output by passing X_test and also stored real target in expected_y. expected_y = y_test predicted_y = model.predict(X_test) Here we have printed r2 score and mean squared log error for the Regressor. print(metrics.r2_score(expected_y, predicted_y)) print(metrics.mean_squared_log_error(expected_y, predicted_y)) plt.figure(figsize=(10,10)) sns.regplot(expected_y, predicted_y, fit_reg=True, scatter_kws={"s": 100}) ## python - valueerror: "the estimator should be a classifier" - data science stack exchange

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I am adapting sklearn-extension ELMClassifier to be accepted as base_estimator to both VotingClassifier and AdaboostClassifier. My code works fine when i use my ELM directly with AdaboostClassifier, but since i'm going to create an Adaboost of different classifiers, i need it to be instantiated inside of VotingClassifier and then pass the VotingClassifier to the Adaboost as a base estimator.

that's probably due to the fact your class is not really 100% compatible to the scikit-learn estimator interface. You can easily verify this with the check_estimator method in sklearn.utils.estimator_checks. This should ensure you it is a proper classifier which can be passed then to AdaBoost.

Normally, inheriting ClassifierMixin is good practice and will provide the attribute _estimator_type = "classifier". In this case there may be complications since ELMClassifier inherits from ELMRegressor; probably the easiest way to work around it is to add estimator_type = "classifier" to your customELMClassifier.

By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. ## how to use xgboost classifier and regressor in python?

Here we have imported various modules like datasets, xgb and test_train_split from differnt libraries. We will understand the use of these later while using it in the in the code snipet. For now just have a look on these imports.

Here we have used datasets to load the inbuilt wine dataset and we have created objects X and y to store the data and the target value respectively. dataset = datasets.load_wine() X = dataset.data; y = dataset.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)

Here, we are using XGBClassifier as a Machine Learning model to fit the data. model = xgb.XGBClassifier() model.fit(X_train, y_train) print(); print(model) Now we have predicted the output by passing X_test and also stored real target in expected_y. expected_y = y_test predicted_y = model.predict(X_test) Here we have printed classification report and confusion matrix for the classifier. print(metrics.classification_report(expected_y, predicted_y)) print(metrics.confusion_matrix(expected_y, predicted_y))

Here we have used datasets to load the inbuilt boston dataset and we have created objects X and y to store the data and the target value respectively. dataset = datasets.load_boston() X = dataset.data; y = dataset.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)

Here, we are using XGBRegressor as a Machine Learning model to fit the data. model = xgb.XGBRegressor() model.fit(X_train, y_train) print(); print(model) Now we have predicted the output by passing X_test and also stored real target in expected_y. expected_y = y_test predicted_y = model.predict(X_test) Here we have printed r2 score and mean squared log error for the Regressor. print(metrics.r2_score(expected_y, predicted_y)) print(metrics.mean_squared_log_error(expected_y, predicted_y)) plt.figure(figsize=(10,10)) sns.regplot(expected_y, predicted_y, fit_reg=True, scatter_kws={"s": 100}) ## python - should i choose random forest regressor or classifier? - cross validated

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I can get the classification directly from randomforestclassifier or I could run randomforestregressor first and get back a set of estimated scores (continuous value). Then I can find a cutoff value to derive the predicted classes out of the set of scores. Both methods can achieve the same goal (i.e. predict the classes for the test data).

First, I really encourage you to read yourself into the topic of Regression vs Classification. Because using ML without knowing anything about it will give you wrong results which you won't realize. And that's quite dangerous... (it's a little bit like asking which way around you should hold your gun or if it doesn't matter)

NO. You don't get probabilities from regression. It just tries to "extrapolate" the values you give (in this case only 0 and 1). This means values above 1 or below 0 are perfectly valid as a regression output as it does not expect only two discrete values as output (that's called classification!) but continuous values.

If you want to have the "probabilities" (be aware that these don't have to be well calibrated probabilities) for a certain point to belong to a certain class, train a classifier (so it learns to classify the data) and then use .predict_proba(), which then predicts the probability.

Just to mention it here: .predict vs .predict_proba (for a classifier!) .predict just takes the .predict_proba output and changes everything to 0 below a certain threshold (usually 0.5) respectively to 1 above that threshold.

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