Название: Python and Machine LearningАвтор: Bernd Klein
Издательство: Bodenseo
Год: 2021
Страниц: 453
Язык: английский
Формат: pdf
Размер: 26.2 MB
Not only in Machine Learning but also in general life, especially business life, you will hear questiones like "How accurate is your product?" or "How precise is your machine?". When people get replies like "This is the most accurate product in its field!" or "This machine has the highest imaginable precision!", they feel fomforted by both answers. Shouldn't they? Indeed, the terms accurate and precise are very often used interchangeably. We will give exact definitions later in the text, but in a nutshell, we can say: Accuracy is a measure for the closeness of some measurements to a specific value, while precision is the closeness of the measurements to each other. These terms are also of extreme importance in Machine Learning (ML). We need them for evaluating ML algorithms or better their results.
We will present in this chapter of our Python Machine Learning Tutorial four important metrics. These metrics are used to evaluate the results of classifications. The metrics are:
- Accuracy
- Precision
- Recall
- F1-Score
We will introduce each of these metrics and we will discuss the pro and cons of each of them. Each metric measures something different about a classifiers performance. The metrics will be of outmost importance for all the chapters of our Machine Learning tutorial.
Contents:
Machine Learning Terminology ...........3
Representation and Visualization of Data ............15
Loading the Iris Data with Scikit-learn ................18
Visualising the Features of the Iris Data Set.........23
Scatterplot 'Matrices ..............27
Datasets in sklearn.................29
Loading Digits Data.................31
Reading the data and conversion back into 'data' and 'labels'.............51
Other Interesting Distributions .............54
k-Nearest-Neighbor Classifier...............72
From Dividing Lines to Neural Networks............96
Neural Networks, Structure, Weights and Matrices ...........141
Running a Neural Network with Python ............153
Backpropagation in Neural Networks ................162
Training a Neural Network with Python ............169
Softmax as Activation Function .........................182
Confusion Matrix...............3
Neural Network ...........198
Multiple Runs ............210
With Bias Nodes...........216
Networks with multiple hidden layers................227
Networks with multiple hidden layers and Epochs ............231
A Neural Network for the Digits Dataset ...........269
Naive Bayes Classifier with Scikit................316
Regression Trees...............413
The maths behind regression trees.............418
Regression Decision Trees from scratch in Python...............423
Regression Trees in sklearn............434
TensorFlow ............437
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