Feature Scaling

What is Feature Scaling? Feature Scaling is an important pre-processing step for some machine learning algorithms. Imagine you have three friends of whom you know the individual weight and height. You would like to deduce Christian’s  t-shirt size from David’s and Julia’s by looking at the height and weight. Name Height in m Weight in…

Receiver Operating Characteristic

ROC Curve As we already introduced Precision and Recall  the ROC curve is another way of looking at the quality of classification algorithms. ROC stands for Receiver Operating Characteristic The ROC curve is created by plotting the true positive rate (TPR) on the y-axis against the false positive rate (FPR) on the x-axis at various…

Python Pipfile and pipenv

  If You already read Python pip and virtualenv you are familiar with the way python handles requirements. but lo and behoild there is a new kid in town or actually two new kids on the block: Pipfile and Pipenv – both with with a capital “P”. If you are tired of creating and maintaining…

Intro to OpenCV with Python

Installation To work with OpenCV from python, you need to install it first: pip install opencv-python Reading Images from file After we import cv2 we can directly work with images like so: import cv2 img = cv2.imread(“doc_brown.png”) For showing the image, it is recommended to use matplotlib import matplotlib.pyplot as plt img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)…

Python data classes

A cool new feature made its way into Python 3.7: Data classes. When You’ve already read my article about Lombok the concept isn’t so new at all: With the new decorator @dataclass You can save a huge amount of time because the methods __init__() __repr__() __eq__() are created for you! from dataclasses import dataclass @dataclass…

New Blog Post

Confusion Matrix

Too confused of the confusion matrix? Let me bring some clarity into this topic! Let’s take the example from Precision and Recall: y_true = [“dog”, “dog”, “non-dog”, “non-dog”, “dog”, “dog”] y_pred = [“dog”, “non-dog”, “dog”, “non-dog”, “dog”, “non-dog”] When we look at the prediction we can count the correct and incorrect classifications: dog correctly classified…