Feature selection is a crucial part of the machine learning workflow. How well the features were selected directly related to the model's performance. So I wrote a handful Python library called OptimalFlow with an ensemble feature selection module in it, called autoFS to simplify this process easily. OptimalFlow is an Omni-ensemble Automated Machine Learning toolkit, which is based on Pipeline Cluster Traversal Experiment(PCTE) approach, to help data scientists building optimal models in easy way, and automate Machine Learning workflow with simple codes. You could read another story of its introduction: "An Omni-ensemble Automated Machine Learning -- OptimalFlow".
This year may be the year that automated machine learning (AutoML) enters the data science vernacular. KDnuggets recently wrote a comprehensive review of the state of AutoML in 2017, AirBnB described how AutoML has accelerated their data scientists' productivity, and the International Conference on Machine Learning (ICML) hosted another workshop on AutoML in August. In this post, I share an AutoML setup to train and deploy pipelines in the cloud using Python, Flask, and two AutoML frameworks that automate feature engineering and model building. To jump straight to the code, check out the GitHub repository. AutoML is a broad term and technically could encompass the entire data science cycle from data exploration to model building.
Back in 2011, Marc Andreesen said, "Software is eating the word". These words have become even more relevant as digitization becomes an organizational priority for enterprises across sectors. The world is becoming software-driven and for the IT department, this has translated into increased demand for software to address ever-evolving requirements. Users have high expectations of usability and demand greater flexibility in business operations. Frequent updates and upgrades are our new normal.
Summary: We are entering a new phase in the practice of data science, the'Code-Free' era. Like all major changes this one has not sprung fully grown but the movement is now large enough that its momentum is clear. Here's what you need to know. We are entering a new phase in the practice of data science, the'Code-Free' era. Like all major changes this one has not sprung fully grown but the movement is now large enough that its momentum is clear.
-- Predicting s ales opportunities outcome is a core to successful business management and revenue forecasting . Conventionally, this prediction has relied mostly on subjective human evaluations in the process of business to business (B2B) sales decision making. Here, we proposed a practical Machine Learning (ML) workflow to empower B2B sales outcome (win/lose) pre diction within a cloud - based computing platform: Microsoft Azure Machine Learning Service (Azure ML). This workflow consists of two pipelines: 1) a n ML pipeline that trains probabilistic predictive models in parallel on the closed sales opportunities data enhanced with an extensive feature engineering procedure for automated selection and parameterization of an optimal ML model and 2) a Prediction pipeline that uses the optimal ML model to estimate the likelihood of win n ing new sales opportunities as well a s predicting their outcome using optimized decision boundaries. The p erformance of the proposed workflow was evaluated on a real sales dataset of a B2B consulting firm. In the Business to Business (B2B) commerce, companies compete to win high - valued sales opportunities to maximize their profitability. In this regard, a key factor for maintain ing a successful B2B business is the task of determining the outcome of sales opportunities.