AI News, Predictive modeling, supervised machine learning, and pattern classification

Predictive modeling, supervised machine learning, and pattern classification

When I was working on my next pattern classification application, I realized that it might be worthwhile to take a step back and look at the big picture of pattern classification in order to put my previous topics into context and to provide and introduction for the future topics that are going to follow.

Pattern classification and machine learning are very hot topics and used in almost every modern application: Optical Character Recognition (OCR) in the post office, spam filtering in our email clients, barcode scanners in the supermarket … the list is endless. In

this article, I want to give a quick overview about the main concepts of a typical supervised learning task as a primer for future articles and implementations of various learning algorithms and applications.

Regression models are based on the analysis of relationships between variables and trends in order to make predictions about continuous variables, e.g., the prediction of the maximum temperature for the upcoming days in weather forecasting. In

To not get lost in all possibilities, the main focus of this article will be on “pattern classification”, the general approach of assigning predefined class labels to particular instances in order to group them into discrete categories.

When we are dealing with a new dataset, it is often useful to employ simple visualization techniques for explanatory data analysis, since the human eye is very powerful at discovering patterns.

However, sometimes we have to deal with data that consists of more than three dimensions and cannot be captured in a single plot: One way to overcome such limitations could be to break down the attribute set into pairs and create a scatter plot matrix.

Looking at those plots above, the scatter plots and (1D) histograms in particular, we can already see that the petal dimensions contain more discriminatory information than the sepal widths and lengths based on the smaller overlap between the three different flower classes.

In this case, a first pre-processing step (feature extraction) could involve the scaling, translation, and rotation of those images in order to obtain the dimensions of the sepals and petals in centimeters.

Occlusion of the leaves could be a problem that might lead to missing data: Many machine learning algorithms won’t work correctly if data is missing in a dataset so that “ignoring” missing data might not be an option.

If the sparsity (i.e., the amount of empty cells in the dataset) is not too high, it is often recommended to remove either the samples rows that contain missing values, or the attribute columns for which data is missing.

Assuming that we extracted certain features (here: sepal widths, sepal lengths, petal widths, and petal lengths) from our raw data, we would now randomly split our dataset into a training and a test dataset.

k-fold cross-validation, the original training dataset is split into k different subsets (the so-called “folds”) where 1 fold is retained as test set, and the other k-1 folds are used for training the model.

E.g., if we set k equal to 4 (i.e., 4 folds), 3 different subsets of the original training set would be used to train the model, and the 4th fold would be used for evaluation.

Normalization and other feature scaling techniques are often mandatory in order to make comparisons between different attributes (e.g., to compute distances or similarities in cluster analysis), especially, if the attributes were measured on different scales (e.g., temperatures in Kelvin and Celsius);

Another common approach is the process of (z-score) “standardization” or “scaling to unit-variance”: Every sample is subtracted by the attribute’s mean and divided by the standard deviation so that the attribute will have the properties of a standard normal distribution (μ=0, σ=1).

One important point that we have to keep in mind is that if we used any normalization or transformation technique on our training dataset, we’d have to use the same parameters on the test dataset and new unseen data.

practice, the key difference between the terms “feature selection” and “dimensionality reduction” is that in feature selection, we keep the “original feature axis”, whereas dimensionality reduction usually involves a transformation technique.

For example, if we’d have a whole bunch of attributes that describe our Iris flowers (color, height, etc.), feature selection could involve the reduction of the available data to the 4 measurements that describe the petal and sepal dimensions.

very simple decision tree for the iris dataset could be drawn like this: Hyperparameters are the parameters of a classifier or estimator that are not directly learned in the machine learning step from the training data but are optimized separately.

convenient tool for performance evaluation is the so-called confusion matrix, which is a square matrix that consists of columns and rows that list the number of instances as “actual class” vs.

Applications Journal, 2001)_ “This book is the unique text/professional reference for any serious student or worker in the field of pattern recognition.” (Mathematical Reviews, Issue 2001) Although this review was published almost 15 years ago, it is still an excellent article that is really worth reading: Jain, Anil K., Robert P.

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