Contents
How to tackle any classification problem end to end?
We will thus deal with binary classification for the sake of simplicity. Also, it is seen that most of the classification problems are binary classification problems. Multi-class classification (classifying digits from 0 to 9) will be dealt with in another article. Handling non-numeric data (handling categorical, ordinal variables and strings)
How are missing values treated as separate categories?
Missing values can be treated as a separate category by itself. We can create another category for the missing values and use them as a different level. This is the simplest method. Prediction models: Here, we create a predictive model to estimate values that will substitute the missing data.
How to solve the problem of overfitting in classification?
Feature selection will remove the problem of overfitting from your classification models. Though there are many methods for feature selection or variable reduction, we shall depict only few very effective and popular ways for feature selection. B.4.1. If you need a specific number of features: SelectKBest class method.
Do you have to have numeric data for classification?
All your input data (X_train or X_test data) must be numeric data before you can do classification! However, from your experience, you must know by now, that this will not always be the case. You will have non-numeric data too most of the times. Non-numeric data can be of 3 types: categorical, ordinal and string data.
How to solve a multi label classification problem?
In this method, we will try to transform our multi-label problem into single-label problem (s). This method can be carried out in three different ways as: This is the simplest technique, which basically treats each label as a separate single class classification problem. For example, let us consider a case as shown below.
How is multi class classification different from binary classification?
Unlike binary classification, multi-class classification does not have the notion of normal and abnormal outcomes. Instead, examples are classified as belonging to one among a range of known classes. The number of class labels may be very large on some problems.
What are the parts of a classification tutorial?
This tutorial is divided into five parts; they are: 1 Classification Predictive Modeling. 2 Binary Classification. 3 Multi-Class Classification. 4 Multi-Label Classification. 5 Imbalanced Classification.