Domino Data Lab

DECEMBER 2, 2020

For a more contrived but impressive illustration of DBSCAN’s capabilities, let’s consider a toy example informally known as the “half-moons” dataset where each data point belongs to one of the two “half-moons”. from sklearn.datasets import make_moons #moons_X: Data, moon_y: Labels moons_X, moon_y = make_moons(n_samples = 2000).

Metrics 116

Metrics KDD Testing Machine Learning 116

Domino Data Lab

SEPTEMBER 9, 2019

Data science teams in industry must work with lots of text, one of the top four categories of data used in machine learning. That’s excellent for supporting really interesting workflow integrations in data science work. Usually it’s human-generated text, but not always. for a, b in pairs:?.

Deep Learning 110

Deep Learning Machine Learning Data Science Visualization 110

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

1: The Himalaya mountain range Much of the data available to us in big data settings is large not only in the number of observations, but also in the number of features. With more features come more potential post hoc hypotheses about what is driving metrics of interest, and more opportunity for exploratory analysis.

Experimentation 61

Experimentation Testing Statistics Metrics 61

Cloudera

APRIL 4, 2019

Use customer lifetime value data to segment customers and tailor offerings to meet their desires and goals. Two very important things happened during the economic slowdown of the mid to late 2000’s. A few years ago a customer service representative took a call with a customer that lasted 10 hours and 29 minutes!

Insurance 55

Insurance IT Interactive Cost-Benefit 55

Domino Data Lab

AUGUST 29, 2019

class DQNAgent: def __init__(self, state_size, action_size): self.state_size = state_size self.action_size = action_size self.memory = deque(maxlen=2000) self.gamma = 0.95 If the episode ends, then we print summary metrics on the episode (see Figures 13.8 Fitness : An overall summary metric that takes into account the above four.

Optimization 111

Optimization Modeling Deep Learning Metrics 111

Sisense

FEBRUARY 24, 2021

In fact, in a 2019 edition of Industrial Management & Data Systems, a research team led by Yu Nie noted that prior to the year 2000, there were only six chief data officers in the world. Clearly, data is becoming more important to organizations. The role of the chief data officer.

Data Warehouse 69

Data Warehouse Big Data Metrics Visualization 69

Sisense

SEPTEMBER 3, 2019

Think of it like something that houses the metrics used to power daily, weekly, or monthly business KPIs. roll-ups of many rows of data). This results in less joins between the metric data in fact tables, and the dimensions. These combine the dimensions and fact tables and pre-aggregate the metrics to simplify reporting.

Data Warehouse 78

Data Warehouse OLAP Statistics Cost-Benefit 78

Jet Global

MAY 9, 2024

Data pipelines are designed to automate the flow of data, enabling efficient and reliable data movement for various purposes, such as data analytics, reporting, or integration with other systems. It also includes data validation and quality checks to ensure the accuracy and integrity of the data being processed.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52