AWS Big Data

AUGUST 7, 2023

Forecasting acts as a planning tool to help enterprises prepare for the uncertainty that can occur in the future. The data contains measurements of electric power consumption in different households for the year 2014. Prepare the data Refer to the following notebook for the steps needed to create this use case.

Forecasting 80

Forecasting Data Warehouse Machine Learning Modeling 80

The Unofficial Google Data Science Blog

APRIL 23, 2024

the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., Crucially, it takes into account the uncertainty inherent in our experiments. Figure 2: Spreading measurements out makes estimates of model (slope of line) more accurate.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

The Unofficial Google Data Science Blog

APRIL 17, 2017

Quantification of forecast uncertainty via simulation-based prediction intervals. First, the system may not be understood, and even if it was understood it may be extremely difficult to measure the relationships that are assumed to govern its behavior. Crucially, our approach does not rely on model performance on holdout samples.

Forecasting 83

Forecasting Modeling Statistics Uncertainty 83

CIO Business Intelligence

FEBRUARY 27, 2023

The IT sector in Ukraine had stabilized after the 2014 Russian incursion with growth accelerating beginning in 2017 and “supercharging” in 2020 and 2021, says Katie Gove, senior director-analyst in Gartner’s Technology and Service Provider Research division. One year later, the industry’s remarkable resilience is clear.

Software 81

Software Marketing Uncertainty Technology 81

The Unofficial Google Data Science Blog

MARCH 13, 2017

Typically, causal inference in data science is framed in probabilistic terms, where there is statistical uncertainty in the outcomes as well as model uncertainty about the true causal mechanism connecting inputs and outputs. CoRR, 2014. [2] 2009, " Measuring invariances in deep networks ". Going deeper with convolutions.

IT 68

IT Visualization Modeling Uncertainty 68