What does Scoring Data Mean?
Note: In data science, there are two types of scoring: model scoring and scoring data. This article is about the latter type.
In machine learning, scoring is the process of applying an algorithmic modelbuilt from a historical dataset to a new dataset in order to uncover practical insights that will help solve a business problem.
Model development is generally a two-stage process. The first stage is training and validation, during which you apply algorithms to data for which you know the outcomes to uncover patterns between its features and thetarget variable. The second stage is scoring, in which you apply the trained model to a new dataset. Then, the model returns outcomes in the form of probability scores for classification problems and estimated averages forregression problems. Finally, youdeploythe trained model into a production application or use the insights it uncovers to improve business processes.
For example, to score a model meant to predict the likelihood of customer churn:
- Build a churn behavior model using a historical dataset that contains information on which customers churned and other information that you believe contributed to that outcome.
- Apply the model to existing customer data to produce a value, or “score,” that estimates their likelihood to churn.
Different ways to score models include:
- Batch scoring. Useful for when the model’s decisions don’t have to be implemented immediately. For example, a marketer may batch score a model on a list of purchased leads to determine which are most likely to buy their product.
- Real-time scoring. Useful when time is of the essence in realizing value from the model. For example, a bank needs a fraud model to score credit card transactions within milliseconds to quickly deny likely fraudulent transactions.
Scoring is also used to evaluate existing models. By training the model on historical data, using it to score other historical data for which you know the outcome, and comparing the scores to the known values, you determine how well the model performs.
Why is Scoring Important?
Scoring is a key component of understanding machine learning model outcomes and choosing the most accurate model that produces the most valuable insights. Once you have a model in production scoring new data, you’ll uncover insights that you can use to create business value.
Using the above example, the model scores identify which current customers are at a high risk of churning, enabling you to plan outreach or special offers to prevent that from happening.
Scoring + DataRobot
DataRobot’s Prediction Explanations feature has a great visualization of model output scores:
DataRobot shows the score in the second column from the left after the individual record ID.
In the above example, a hospital has built a classification model to determine the likelihood that a patient will be readmitted in 30 days or less. The model’s score for patientID 9155 is 0.888. In other words, this patient had an 88.8% likelihood of being readmitted prior to the end of 30 days. The “Explanations” columns list the top factors that contributed to that probability score.
Using the model score, the hospital can take action to reduce the probability of readmissions, which might include delaying the discharge of patients that are similar to those with high readmission scores, resulting in better patient outcomes and a lower instance of the hospital being fined.
For information on how DataRobot handles scoring and deployment, see the Deployment wiki entry.
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FAQs
In machine learning, scoring is the process of applying an algorithmic model built from a historical dataset to a new dataset in order to uncover practical insights that will help solve a business problem.
What is a data score? ›
Data quality scores are computed based on quality dimensions for each individual column in the data set, and then a combined quality score for the entire data set is calculated. The combined score is an average of the scores for all columns.
What is scoring in research? ›
A scoring system in research refers to a method or tool used to assign scores or ratings to certain aspects or components of a research study. These scores are used to assess the credibility, quality, or level of performance of the research.
What is a scoring model? ›
A scoring model is a tool you use to assign a comparative value to one or more projects or tasks. Scoring models allow governance teams to rank potential projects based on criteria such as risk level, cost, and potential financial returns.
What is scoring analysis? ›
analysis is defined as an innovative strategic planning technique to identify an organization's Strengths, Challenges, Opportunities, Risks, Environment and Systems. From a high-level perspective, this technique can provide innumerable advantages to achieve a wide range of organizational goals.
What is scoring in data analytics? ›
In machine learning, scoring is the process of applying an algorithmic model built from a historical dataset to a new dataset in order to uncover practical insights that will help solve a business problem.
What is a score in a data set? ›
A mean score, also known as the arithmetic average, is calculated by adding up all the values in a set of numbers and then dividing by the total number of values. The mean score is often used as a measure of central tendency, which represents the typical or most representative value in a dataset.
What is scoring method? ›
A scoring method is the technique or formula that you will use to calculate the score for each proposal, such as adding, multiplying, or averaging the ratings for each criterion. For example, you might use a 1 to 5 scale and multiply the ratings by the weights to get the score for each proposal.
What are the three types of scoring? ›
Types of Scoring
- Analytic Scoring. In this mode, students' writing is evaluated based on detailed grades for elements of writing such as vocabulary, grammar, composition, or mechanics. ...
- Holistic Scoring. ...
- Primary Trait Scoring.
Scoring for assessment is focused on patterns of strengths and weaknesses in a group of students for use by program-level decision makers. When grading is used for assessment, a second process of identifying patterns among students is necessary.
Scoring Methodology means the range of marks that may be given to a Potential Provider depending on the quality of its response to a question which is located in the boxes below the applicable question; Sample 1.
What are two most commonly used scoring models? ›
FICO and VantageScore are both popular credit scoring models. Lenders use credit scoring in risk-based pricing in which the terms of a loan, including the interest rate, offered to borrowers are based on the probability of repayment.
What is quantitative scoring model? ›
1.51 Quantitative Scoring Model
Quantitative analysis involves, an assessment of the financial position based on the customer's income and monthly expenses. It may also include a cash flow analysis of the customer's accounts and credit history.
What is the analytic scoring method? ›
A method of evaluating written text that assigns individual scores to separate aspects of writing quality, such as organization, ideas, sentence structure, word choice, and mechanics. Writing skills are essential for success in K-12 and post-secondary settings.
What is a scoring measure? ›
More Technical Details on Scoring. Measure Score: A Measure score is a summary indicator describing how teachers or students responded to the specific questions making up each Measure; a number of Measures together comprise an Essential. A Measure score is calculated by using a method called Rasch analysis.
What is a score analysis? ›
What Is Score Analysis? A score analysis provides insight regarding individual performance on each domain of an examination. A separate analysis is available for each failed exam, to prepare an examinee for future testing and is provided as a courtesy.
What is the mean score of the data? ›
The mean, which is also known as the average, is the total sum of values in a sample divided by the number of values in your sample. [1] For example, to figure out a grade at the end of a course, you calculate the mean of all of your test scores.
What is the score for data analytics? ›
Previously, the Data Analytics Framework used a 600-850 score range. Now, when you complete this Framework your coding score will range from 200-600.
What is the difference between variable and score? ›
A variable is a characteristic or condition that changes or has different values for different individuals. A z-score specifies the precise location of each X value within a distribution. The sign of the z-score (+ or -) signifies whether the score is above the mean or below the mean.
What is a good number of data points? ›
You need at least thirty data points to get a statistically significant result. One data point tells you nothing. In a business decision, the monetary value of data is more important than its statistical significance.
