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.
Related
I have learned that Sklearn treats multi-class classification problems as a collection of binary problems. Quoting the Sklearn user guide:
In extending a binary metric to multiclass or multilabel problems, the data is treated as a collection of binary problems, one for each class.
So, binary classification models like LogisticRegression or Support vector matrices can support multi-class cases by using either One-vs-One or One-vs-Rest strategies. I wanted to know if that was the case for RandomForestClassifier too? How about other classifiers in Sklearn - are they all used as binary classifiers under the hood when dealing with a multi-class problem?
According to the documentation for Decision Trees, multi-output problems add a small change to the leaves of each tree in a random forest.
Suppose you have set criterion='gini'. In essence, each node is built by picking a subset of max_features features, calculating the average reduction in the gini impurity for all N classes and choosing the variable-threshold combination that reduces it most.
This means that random forests do not create one model for each class. Instead, it's only one model that simultaneously reduces the criterion metric for all classes in each node of every tree and predicts the most common class at each leaf.
I would like to compare the accuracies of running logistic regression on a dataset following PCA and LDA. The dataset I am using is the wisconsin cancer dataset, which contains two classes: malignant or benign tumors and 30 features. I have already conducted PCA on this data and have been able to get good accuracy scores with 10 PCAs. I know that LDA is similar to PCA. My understanding is that you calculate the mean vectors of each feature for each class, compute scatter matricies and then get the eigenvalues for the dataset. Is LDA similar to PCA in the sense that I can choose 10 LDA eigenvalues to better separate my data? I have tried LDA with scikit learn, however it has only given me one LDA back. Is this becasue I only have 2 classes, or do I need to do an addiontional step? I would like to have 10 LDAs in order to compare it with my 10 PCAs. Is this even possible?
Actually both LDA and PCA are linear transformation techniques: LDA is a supervised whereas PCA is unsupervised (ignores class labels). You can picture PCA as a technique that finds the directions of maximal variance.And LDA as a technique that also cares about class separability (note that here, LD 2 would be a very bad linear discriminant).Remember that LDA makes assumptions about normally distributed classes and equal class covariances (at least the multiclass version; the generalized version by Rao).
I'm following a TensorFlow example that takes a bunch of features (real estate related) and "expensive" (ie house price) as the binary target.
I was wondering if the target could take more than just a 0 or 1. Let's say, 0 (not expensive), 1 (expensive), 3 (very expensive).
I don't think this is possible as the logistic regression model has asymptotes nearing 0 and 1.
This might be a stupid question, but I'm totally new to ML.
I think I found the answer myself. From Wikipedia:
First, the conditional distribution y|x is a Bernoulli distribution rather than a Gaussian distribution, because the dependent variable is binary. Second, the predicted values are probabilities and are therefore restricted to (0,1) through the logistic distribution function because logistic regression predicts the probability of particular outcomes.
Logistic Regression is defined for binary classification tasks.(For more details, please logistic_regression. For multi-class classification problems, you can use Softmax Classification algorithm. Following tutorials shows how to write a Softmax Classifier in Tensorflow Library.
Softmax_Regression in Tensorflow
However, your data set is linearly non-separable (most of the time this is the case in real-world datasets) you have to use an algorithm which can handle nonlinear decision boundaries. Algorithm such as Neural Network or SVM with Kernels would be a good choice. Following IPython notebook shows how to create a simple Neural Network in Tensorflow.
Neural Network in Tensorflow
Good Luck!
I am participating in the Kaggle San Francisco Crime competition and i am currently trying o number of different classifiers to test benchmark performances. I am using a LogisticRegressionClassifier from sklearn, without any parameter tuning and I noticed from sklearn.metrict.classification_report that it is only predicting the predominant classses,i.e. the classes which have the highest number of occurrences in my training set.
Intuition tells me that this has to parameter tuning, but I am not sure which parameters I have to tweek in order to make the classifier more aware of less predominant classes ( LogisticRegressionClassifier has quite a few ). At the moment it is predicting only 3 classes from 38 or smth like that so it definitely needs improvement.
Any ideas?
If your model is classifying only predominant classes then you are facing problem of imbalance classes. Here are some good reads to tackle this in machine learning.
Logistic Regression is a binary classifier and uses one-vs-all or one-vs-one technique for multiclass classification, which is not good if you have higher number of output classes (33 in your case). Try using other classifier. For a start , use softmax classifier which is an extension of logistic classifier having support for multi-class classification. In scikit learn, set multi_class variable as multinomial to use softmax regression.
Other way to improve your model could be using GridSearch for parameter tuning.
On a side note, I would recommend you to use other models as well.
I am developing an OCR using SVM in opencv C++. SVM used is (one vs one multi-class) linear SVM. Opencv svm (multi-class)doesn't give probability estimates for each class that was used in time of training. So i tried my luck with libsvm Multi-class classification (and probability output) via error-correcting codes. It gave me the probability estimates for each of the class, now I want to use the training model file in opencv C++. I get an error. Now my problem is how to use the training model in opencv, if not possible how to get probability estimates for each of the class using (one vs one multi-class) linear svm ?