I am trying to perform logistic regression for my data. I came to know about glm. What is the actual difference between glm and regular logistic regression?
What are the pros and cons of it?
Logistic Regression is a special case of Generalized Linear Models. GLMs is a class of models, parametrized by a link function. If you choose logit link function, you'll get Logistic Regression.
The main benefit of GLM over logistic regression is overfitting avoidance. GLM usually try to extract linearity between input variables and then avoid overfitting of your model. Overfitting means very good performance on training data and poor performance on test data.
Related
In TensorFlow library, what does the tf.estimator.LinearClassifier class do in linear regression models? (In other words, what is it used for?)
Linear Classifier is nothing but Logistic Regression.
According to Tensorflow documentation, tf.estimator.LinearClassifier is used to
Train a linear model to classify instances into one of multiple
possible classes. When number of possible classes is 2, this is binary
classification
Linear regression predicts a value while the linear classifier predicts a class. Classification aims at predicting the probability of each class given a set of inputs.
For implementation of tf.estimator.LinearClassifier, please follow this tutorial by guru99.
To know about the linear classifiers, read this article.
while selecting features in machine learning, one can use Lasso regression to figure out the least required feature by selecting the least coefficient but we can do the same using Linear Regression
linear regression
Y=x0+x1b1+x2b2.......xnbn
here x1,x2,x3...xn are coefficient, using gradient descent we get the best coefficient, we can remove the features who has the least coefficient. now when it is possible using Linear Regression then why should one use Lasso Regression?
am i missing something, please help
Lasso is a regularization technique which is for avoiding overfitting when you train your model. When you do not use any regularization technique, your loss function just tries to minimize the difference between the predicted value and real value min |y_pred - y|.
To minimize this loss function, gradient descent changes the coefficient of your model. This step may cause the overfitting of your model because your optimization function want only to minimize the difference between prediction and real value. To solve this issue, regularization techniques add another penalty term to the loss functions: value of coefficients. In this way, when your model tries to minimize the difference between predicted and real value, it also tries to do not increase the coefficients too much.
As you mentioned, you can select features in both ways, however, Lasso technique also takes care of the overfitting problem.
I am sure this question may not be in the brilliant category. But Somehow to learn machine learning i may start with stupid question. So, please.
I understood the terms of regressions partially.
The regression essentially give the idea of the relationship between the dependent and independent variables.
If the dependent variable is continuous and if you see the linear relation between dependent and independent, then linear regression is a way to go.
A slight change now. If the dependent value could be something like Binary value (Y/N), ie: the output value is binomial distribution, then logistic regression is a way to go that which demands non linear relationship between dependent and independent.
So far..Please correct me if i am wrong.
Now my question is with respect to ordinal logistic regression.
I have started looking at the below link for reference
https://statistics.laerd.com/spss-tutorials/ordinal-regression-using-spss-statistics.php
Where it is mentioned that " It can be considered as either a generalisation of multiple linear regression or as a generalisation of binomial logistic regression".
Could someone help me understand this above statement with examples?
Logistic regression can be considered as an extension of linear regression. But instead of predicting continuous variables, it predicts discrete variables by introducing the computation of an activation function. So, you are asked to produce a discriminatory function that based on X you produce a function that outputs f: [1,2, ..., k] where k is the number of classes that your problem presents. Now X can be composed of features that are both continuous or discrete. It does not matter, just make sure you apply pre-processing to them.
The base case for logistic regression is finding the decision boundary that divides two classes. But in order to add more classes, you have to implement another approach. There are several: softmax (https://en.wikipedia.org/wiki/Softmax_function), one-vs-all (https://en.wikipedia.org/wiki/Multiclass_classification), etc.
Finally, answering your question about ordinal logistic regression is an extension of logistic regression. But considers the order of the output variables such as in the case of a test. Take a look online for examples.
I am a beginner in machine learning field and I want to learn how to do multiclass classification with Gradient Boosting Tree (GBT). I have read some of the articles about GBT but for regression problem and I couldn't find the right explanation about GBT for multiclass classfication. I also check GBT in scikit-learn library for machine learning. The implementation of GBT is GradientBoostingClassifier which used regression tree as the weak learners for multiclass classification.
GB builds an additive model in a forward stage-wise fashion; it allows for the optimization of arbitrary differentiable loss functions. In each stage n_classes_ regression trees are fit on the negative gradient of the binomial or multinomial deviance loss function. Binary classification is a special case where only a single regression tree is induced.
Source : http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#sklearn.ensemble.GradientBoostingClassifier
The things is, why do we use regression tree as our learners for GBT instead of classification tree ? It would be very helpful, if someone can provide me the explanation about why regression tree is being used rather than classification tree and how regression tree can do the classification. Thank you
You are interpreting 'regression' too literally here (as numeric prediction), which is not the case; remember, classification is handled with logistic regression. See, for example, the entry for loss in the documentation page you have linked:
loss : {‘deviance’, ‘exponential’}, optional (default=’deviance’)
loss function to be optimized. ‘deviance’ refers to deviance (= logistic regression) for classification with probabilistic outputs. For loss ‘exponential’ gradient boosting recovers the AdaBoost algorithm.
So, a 'classification tree' is just a regression tree with loss='deviance'...
I have data suited to multinomial logistic regression but I don't know how to formulate the model in predicting my Y.
How do I perform Multinomial Logistic Regression using SPSS?
How does stepwise method work?
There are plenty of examples of annotated output for SPSS multinomial logistic regression:
UCLA example
My own list of links and resources
Stepwise method provides a data driven approach to selection of your predictor variables. In general the decision to use data-driven or direct entry or hierarchical approaches is related to whether you want to test theory (i.e., direct entry or hierarchical) or you want to simply optimise prediction (i.e., stepwise and related methods).