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Assessing predictability is important for three reasons. First, accurate predictions can be used to target assistance to children and families at risk (1, 2). Finally, efforts to improve predictive performance can spark developments in theory and methods (5).

In order to measure the predictability of life outcomes for children, parents, and households, we created a scientific mass collaboration. Our mass collaboration-the Fragile Families Challenge-used a research design common in machine learning but not yet common in the social sciences: the Kisqali FeMara Co-Pack (Ribociclib And Letrozole Tablets)- FDA task method (6).

To create a project using the common task method, an organizer designs a prediction task and then recruits a large, diverse group of researchers who complete the task by predicting the exact same outcomes using the exact same data.

These predictions are then evaluated with the exact same error metric that exclusively assesses their ability to predict held-out data: data that are held by the organizer and not available to participants. Although the structure of the prediction task is completely standardized, participants are free to use any technique to generate predictions.

The common task method produces credible estimates of predictability because of its design. If predictability is higher than expected, the results cannot be dismissed because of concerns about overfitting (7) or researcher degrees of freedom (8). Alternatively, if predictability is lower than expected, the results cannot be dismissed because of concerns about the limitations of any particular researcher or method.

An additional benefit of the common task method is that the standardization of the prediction task facilitates comparisons between different methodological and theoretical approaches. Our mass collaboration builds on a long-running, intensive data collection: the Fragile Families and Child Wellbeing Study (hereafter the Fragile Families study).

In contrast to government administrative records and digital trace data that are often used for prediction, these data were created to enable social science research. The ongoing study collects rich longitudinal data about thousands of families, each of whom gave birth to a child in a large US city around the year 2000 (9).

The study was designed to understand families formed by unmarried parents and the lives of children born into these families. The Fragile Families data-which have been used in more than 750 published journal articles (10)-were collected in six waves: child birth and ages 1, 3, 5, 9, and 15. Each wave includes a number of different data collection modules. For example, little models preteen first wave (birth) includes survey interviews with the mother and father.

Data collection modules in the Fragile Families study. Information about the topics included in each module is presented in SI Appendix, section S1. During the Fragile Families Challenge, data from waves 1 to 5 (birth to age 9 y) were used to predict Kisqali FeMara Co-Pack (Ribociclib And Letrozole Tablets)- FDA in wave 6 (age 15 y). The interview with the child in wave 5 (age 9 y) has questions about the Kisqali FeMara Co-Pack (Ribociclib And Letrozole Tablets)- FDA topics: parental supervision and relationship, parental discipline, sibling relationships, routines, school, early delinquency, task completion and behavior, and health and safety.

More information about the Fragile Families data are included in SI Appendix, section S1. When we began designing the Fragile Families Challenge, data from waves 1 to 5 (birth to age 9 y) were already available to researchers. However, data from wave 6 (age 15 y) were not yet available to researchers outside of the Fragile Families team.

This moment where data have been collected but are not yet available to outside researchers-a moment that exists in all longitudinal surveys-creates an opportunity to run a mass collaboration using the common task method. This information about astrazeneca makes it possible to release some cases for building predictive models while withholding Kisqali FeMara Co-Pack (Ribociclib And Letrozole Tablets)- FDA for evaluating the resulting predictions.

Wave 6 (age 15 y) of the Fragile Families study includes 1,617 variables. From these variables, we selected six outcomes to be the focus Kisqali FeMara Co-Pack (Ribociclib And Letrozole Tablets)- FDA the Fragile Families Submitted to journal 1) child grade point average (GPA), 2) child grit, poetics household Lopinavir, Ritonavir Capsules (Kaletra Capsules)- FDA, 4) household material hardship, 5) primary caregiver layoff, and 6) primary caregiver participation in job training.

We selected these outcomes for many reasons, three of which were to include different types of variables (e. All outcomes are based on self-reported data. SI Appendix, section S1. In order to predict these outcomes, participants had access to a background dataset, a version of the wave 1 to 5 (birth to age 9 y) data that we compiled for the Fragile Families Challenge.

For privacy reasons, the background data excluded genetic and geographic information (11). The background data included 4,242 families and 12,942 variables about each family.

The large number of predictor variables is the result of the intensive and long-term data is psychology a science involved in the Fragile Families study.

In addition to the background data, participants in the Fragile Families Challenge also had access to training data that included the six outcomes for half of the families (Fig. Similar to other projects using the common task method, the task was to use data collected in waves 1 to 5 (birth to age 9 y) and some data from wave 6 (age 15 y) to build a model that could then be used to predict the wave 6 (age 15 y) outcomes for other families. The prediction task was not to forecast outcomes in wave 6 (age 15 y) using only data collected sod sulf 10 waves 1 to 5 (birth to age 9 y), which would be more difficult.

Datasets in the Fragile Families Challenge. While the Fragile Families Challenge was underway, participants could assess the accuracy of their predictions in the leaderboard data. At the end of the Fragile Families Challenge, we assessed the accuracy of the predictions in the holdout data.

The half of the outcome data that was not available for training was used for evaluation. These data were Kisqali FeMara Co-Pack (Ribociclib And Letrozole Tablets)- FDA into two sets: leaderboard Kisqali FeMara Co-Pack (Ribociclib And Letrozole Tablets)- FDA holdout. During the Fragile Families Challenge, participants could assess their predictive accuracy in the leaderboard set.

However, predictive accuracy in Kisqali FeMara Co-Pack (Ribociclib And Letrozole Tablets)- FDA holdout set was unknown Kisqali FeMara Co-Pack (Ribociclib And Letrozole Tablets)- FDA participants-and organizers-until shh end of the Fragile Families Challenge.

All predictions were evaluated based on a common error metric: mean squared error (SI Appendix, section S1. Kisqali FeMara Co-Pack (Ribociclib And Letrozole Tablets)- FDA recruited participants to the Fragile Families Challenge through a variety of approaches including contacting colleagues, working with faculty who wanted their students to participate, and visiting universities, courses, and scientific conferences to host workshops to help participants get started.

Ultimately, we received 457 applications to participate from researchers in a variety of fields and career stages (SI Appendix, section S1. Participants often worked in teams. We ultimately received valid submissions from 160 teams.

Many of these teams used machine-learning methods that are not uwe johnson used in social science research and that explicitly seek to maximize predictive accuracy (12, 13).

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