The metrics that you choose to evaluate your machine learning algorithms are very important. Choice of metrics influences how the performance of machine learning algorithms is measured and compared. They influence how you weight the importance of different characteristics in the results and your ultimate choice of which algorithm to choose. In this post you will discover how to select and use different machine learning performance metrics in Python with scikit-learn. Metrics To Evaluate Machine Learning Algorithms in Python Photo by Ferrous Büller, some rights reserved.
Spot-checking is a way of discovering which algorithms perform well on your machine learning problem. You cannot know which algorithms are best suited to your problem before hand. You must trial a number of methods and focus attention on those that prove themselves the most promising. In this post you will discover 6 machine learning algorithms that you can use when spot checking your regression problem in Python with scikit-learn. Spot-Check Regression Machine Learning Algorithms in Python with scikit-learn Photo by frankieleon, some rights reserved.
Neural networks are trained using stochastic gradient descent and require that you choose a loss function when designing and configuring your model. There are many loss functions to choose from and it can be challenging to know what to choose, or even what a loss function is and the role it plays when training a neural network. In this post, you will discover the role of loss and loss functions in training deep learning neural networks and how to choose the right loss function for your predictive modeling problems. Loss and Loss Functions for Training Deep Learning Neural Networks Photo by Ryan Albrey, some rights reserved. A deep learning neural network learns to map a set of inputs to a set of outputs from training data.
After you make predictions, you need to know if they are any good. There are standard measures that we can use to summarize how good a set of predictions actually are. In this tutorial, you will discover how to implement four standard prediction evaluation metrics from scratch in Python. How To Implement Machine Learning Algorithm Performance Metrics From Scratch With Python Photo by Hernán Piñera, some rights reserved. You must estimate the quality of a set of predictions when training a machine learning model.
This article was originally published in February 2016 and updated in August 2019. The idea of building machine learning models works on a constructive feedback principle. You build a model, get feedback from metrics, make improvements and continue until you achieve a desirable accuracy. Evaluation metrics explain the performance of a model. An important aspect of evaluation metrics is their capability to discriminate among model results. I have seen plenty of analysts and aspiring data scientists not even bothering to check how robust their model is. Once they are finished building a model, they hurriedly map predicted values on unseen data. This is an incorrect approach. Simply building a predictive model is not your motive. It's about creating and selecting a model which gives high accuracy on out of sample data.