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Common Machine Learning Obstacles

KDnuggets

In this blog, Seth DeLand of MathWorks discusses two of the most common obstacles relate to choosing the right classification model and eliminating data overfitting.

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Identification of Hazardous Areas for Priority Landmine Clearance: AI for Humanitarian Mine Action

ML @ CMU

Since landmines are not used randomly but under war logic , Machine Learning can potentially help with these surveys by analyzing historical events and their correlation to relevant features. Validation results in Colombia. Each entry is the mean (std) performance on validation folds following the block cross-validation rule.

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Feature selection by random search in Python

KDnuggets

Feature selection is one of the most important tasks in machine learning. Learn how to use a simple random search in Python to get good results in less time.

Python 307
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Predict football punt and kickoff return yards with fat-tailed distribution using GluonTS

Flipboard

With advanced analytics derived from machine learning (ML), the NFL is creating new ways to quantify football, and to provide fans with the tools needed to increase their knowledge of the games within the game of football. Models were trained and cross-validated on the 2018, 2019, and 2020 seasons and tested on the 2021 season.

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How to Make GridSearchCV Work Smarter, Not Harder

Mlearning.ai

Figure 1: Brute Force Search It is a cross-validation technique. This is a technique for evaluating Machine Learning models. Figure 2: K-fold Cross Validation On the one hand, it is quite simple. Running a cross-validation model of k = 10 requires you to run 10 separate models. England, A.

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Identifying defense coverage schemes in NFL’s Next Gen Stats

AWS Machine Learning Blog

Through a collaboration between the Next Gen Stats team and the Amazon ML Solutions Lab , we have developed the machine learning (ML)-powered stat of coverage classification that accurately identifies the defense coverage scheme based on the player tracking data. Advances in neural information processing systems 32 (2019).

ML 98
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Announcing the Winners of Invite Only Data Challenge: OCEAN Twitter Sentiment pt. 2

Ocean Protocol

By this, Mohammed was able to conclude there is a lag in the market reaction to users' sentiments and proceeded to build a machine-learning model that reflected the outcomes of each of the mentioned parameters. This deployed hyperparameters tuning and cross-validation to ensure an effective and generalizable model.