Python3   2201

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[1901.01588] PyOD: A Python Toolbox for Scalable Outlier Detection
PyOD is an open-source Python toolbox for performing scalable outlier detection on multivariate data. Uniquely, it provides access to a wide range of outlier detection algorithms, including established outlier ensembles and more recent neural network-based approaches, under a single, well-documented API designed for use by both practitioners and researchers. With robustness and scalability in mind, best practices such as unit testing, continuous integration, code coverage, maintainability checks, interactive examples and parallelization are emphasized as core components in the toolbox's development. PyOD is compatible with both Python 2 and 3 and can be installed through Python Package Index (PyPI) or
Python3  OutlierDetection 
9 days ago by neuralmarket
Class — Python for you and me 0.3.alpha1 documentation
Before writing your first class, you should know the syntax. We define a class in the following way.
Python3  Programming 
10 days ago by neuralmarket
Introduction to Functional Programming in Python
In the previous example, we were able to count the lines only with the use of functions. When we only use functions, we are applying a functional approach to programming which is, non-excitingly, called functional programming. The concepts behind functional programming requires functions to be stateless, and rely only on their given inputs to produce an output.
Python3  Programming 
10 days ago by neuralmarket
interpretable_machine_learning_with_python/xgboost_pdp_ice.ipynb at master · jphall663/interpretable_machine_learning_with_python
A key to building interpretable models is to limit their complexity. The more complex a model is, the harder it is to explain and understand. Overly complex models can also make unstable predictions on new data, which is both difficult to explain and makes models harder to trust. Monotonicity constraints not only simplify models, but do so in a way that is somewhat natural for human reasoning, increasing the transparency of predictive models. Under monotonicity constraints, model predictions can only increase or only decrease as an input variable value increases, and the direction of the constraint is typically specified by the user for logical reasons. For instance, a model might be constrained to produce only increasing probabilities of a certain medical condition as a patient's age increases, or to make only increasing predictions for home prices as a home's square footage increases.
Python3  H2O  MachineLearning-interpretability 
15 days ago by neuralmarket
Using AWS lambda to run python scripts instead of local cron - AndyPi
This service allows you to run a specific piece of code (Python, Node.js java etc) in response to an AWS event, or at a scheduled time (using AWS Cloudwatch) which you can specify it using a cron expression. The code is serverless – you don’t need to do any management at all, and you are billed per 100ms, only for the time your code executes – so it is extremely cheap! The main disadvantage is that the AWS console is quite complicated at first, and there are a few pitfalls. This walkthrough will identify a couple of the ones I came across.
Python3  AWS  Lambda 
15 days ago by neuralmarket
Sunsetting Python 2 support
We are keen to use Python 3 to its full potential, and we currently accept the cost of writing cross-compatible code to allow a smooth transition, but we don’t intend to maintain this compatibility indefinitely. Although the transition has not been as quick as we hoped, we do see it taking place, with more and more people using, teaching and recommending Python 3.
15 days ago by neuralmarket

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