A lot of points are bunched at both the bottom and top of the visualization, which means that our model is often very confident that a person is low income or high income. These files are accessible using the microdata access tool on data.census.gov and the Census Bureau's FTP site. If you have an ID column and you want to find most repetitive category from another column for each ID then you can use below query, Table: Query: SELECT ID, CATEGORY, COUNT(*) AS FREQ FROM TABLE GROUP BY 1,2 QUALIFY ROW_NUMBER() OVER(PARTITION BY ID ORDER BY FREQ DESC) = 1; Result: use_cache: If set to TRUE (the dfault), data will be read from a temporary local cache for the duration of the R session, if available. They make their datasets openly available on Github. There are definitely more red points than blue points, which means that the model predicts more people as low income than high income. That may lead to the model not learning an accurate representation of the world in which it is trying to make predictions (of course, even if it does learn an accurate representation, is that what we want the model to perpetuate? Is it a bad sign that a rejection email does not include an invitation to apply again in the future? Above: Using WIT in a notebook with a TF Estimator. At each point, we'll highlight statements about the things WIT helped us learn about our dataset and trained model in bold. What can we learn from this initial view? Income Datasets The pages below allow you to download public use microdata from various Census surveys and programs in order to conduct your own statistical analysis. In order to investigate fairness, we need to tell the tool which feature in each datapoint the model is trying to predict, so the tool can see how well the model performs on each datapoint (does it get the prediction right or wrong?). If it is not, iterate your line data and check each item Census Reporter is a Knight News Challenge-funded project to make it easier for journalists to write stories using information from the U.S. Census bureau. More than 56 million people use GitHub to discover, fork, and contribute to over 100 million projects. The What-If Tool is being actively developed and documentation is likely to change as we improve the tool. The ROC curve shows the true positive rate and false positive rate for every possible setting of the positive classification threshold, with the current threshold called out as a highlighted point on the curve. We can see that of the 5,000 test datapoints, over 3,300 are from men and over 4,200 are from caucasions. A set of reasonably clean records was extracted using the following conditions: ((AAGE>16) && (AGI>100) && (AFNLWGT>1)&& (HRSWK>0)) Prediction task is to determine whether a person makes over 50K a year. When during construction of them, did Bible-era Jewish temples become "holy"? Above: Finding demographic parity in the Performance & Fairness tab. Clicking on the education header in the partial dependence plots area opens up the plot for changing the categorical (non-numeric) education feature. 4 Also known as "Census Income" dataset. is the measure of central tendency that represents the most frequently occurring value in the array. It also shows how we can use WIT with non-TensorFlow models by providing a custom predict function for the tool to use. Create notebooks or datasets and keep track of their status here. How to print colored text to the terminal? Sometimes the data may come from a source that contains biases, for instance, human-labeled data that reflects the biases of the humans. How do I get the number of elements in a list? Can someone please help me on how I can achieve it? We can set the x-axis scatter to a feature of the dataset, such as education level. You'll notice there is a button on the tool labeled "demographic parity". Word vectors for 157 languages trained on Wikipedia and Crawl. Recent state-of-the-art English word vectors. In some systems, such as a medical early screening test (where a positive classification would be an indication of a possible medical condition, requiring further medical testing), it is important to be more permissive with lower-scoring datapoints, preferring to predict more datapoints as in the positive class, at the risk of having more false positives (which would then be weeded out by the follow-up medical testing). If you have found one datapoint on which your model is doing something interesting/unexpected, this can be an interesting view to explore other similar datapoints in order to see how the model is performing on them. Suppose the attribute "gender" had been entered into a data set using the values "1" and "2," and we wanted to change the attribute to be "Female" coded as "0" and "1." Back in the "Performance & Fairness" tab, we can set an input feature (or set of features) with which to slice the data. In the Features tab, we can look to see the distribution of values for each feature in the dataset. A cost ratio of 0.25 means that we consider a false negative 4 times as costly as a false positive. When checking the attributes list for emptiness, we # need to subtract 1 to account for the target attribute. These each show how changing each feature individually affects all of the datapoints in the dataset. Remove the scatterplot settings by using "(default)". Leave us a note, feedback, or suggestion on. The mrc dataset contains information on Québec regional county municipalities (MRCs) in a ESRI shapefile format. FAQ. A cost ratio of 3 means that we consider a false positive 3 times as costly as a false negative. add New Notebook add New Dataset. auto_awesome_motion. Join Stack Overflow to learn, share knowledge, and build your career. It involves methods at the intersection machine learning, statistics and data base management systems. JavaScript MIT 126 14 0 0 Updated Jan 6, 2015 Only selected geographic areas are identified in the ACS PUMS, including Region, Division, State, and Public Use Microdata Areas (PUMAs). How to center vertically small (tiny) equation numbered tags? “Least Astonishment” and the Mutable Default Argument. And we can see the nearest counterfactual highlighted in green. What data structure should most_common_elements be? WIT has plenty of other features not included in this walkthrough, such as: This notebook shows how WIT can help us compare two models that predict toxicity of internet comments, one of which has had some de-biasing processing performed on it. Since ML models learn from labeled training data, their inferences will reflect the information contained inside the training data. We can see that at the default threshold level of 0.5, our model is incorrect about 18% of the time, with about 10% of the time being false positives and 8% of the time being false negatives. Is there a more modern version of "Acme", as a common, generic company name? What is the mathematical meaning of the plus sign (+) in chemical reaction equations? All attributes were already tested: remember, each node tests a different feature. If deploying this binary classification model into a real application, this threshold setting might be ideal, assuming we don't wish to penalize false positives and false negatives differently. So the model is clearly learning that there is a positive correlation between education level and being high income. Above: Setup dialog for WIT in TensorBoard. A set of reasonably clean records was extracted using the following conditions: ((AAGE>16) && … Back on the initial view, let's further investigate a datapoint near the decision boundary, where datapoints turn from red to blue. Use of Python to read/ parse a raw dataset, convert data types, apply IF statements, and apply for loops in order to find which US city has the lowest rate of violent crime. By simply changing the age of this person, the model now predicts that they are high income. We can also see that the model predicts high income for females much less than it does for males (10% of the time for females vs 28% of the time for males). Above: A scatterplot of education level versus model inference score. WIT can automatically set the classification threshold to the optimal point, given the dataset loaded and the cost ratio, which specifies the relative cost of a false positive versus a false negative. In this walkthrough, we explore how the What-If Tool (WIT) can help us learn about a model and dataset. Now we can see a positive classification threshold slider, confusion matrix, and ROC curve for the model. Subnational data files include Federal Information Processing System (FIPS) codes, which uniquely identify geographic areas. Our Dataset and Model. Finding datasets for current events can be tricky. Above: The initial scatterplot of results. As a result, points in the top half of the visualization are blue whereas those on the bottom half are red. We can immediately see that as education level increases (as we move right on the plot), the number of blue points increases. This is necessary to get the percentage of positive predictions to be equal between the two groups. Another way to see how changes to a person can cause changes in classification is by looking for a nearest counterfactual to the selected datapoint. Above: The dialog for using similiarity in the datapoints visualization. Was there an organized violent campaign targeting whites ("white genocide") in South Africa? If it is not, iterate your line data and check each item, PS: For Python 2, add from __future__ import print_function on top of your script. For any datapoint that we provide it, the model will return a score between 0 and 1 for how confident it is that the person represented by the datapoint is high income. The first part of the workshop is to use the UCI Machine Learning Repository to find a non-trivial dataset with which to build a model. We will show later in this tutorial how to change this threshold. Let's try changing the age from 42 to 48 and clicking the "Run inference" button. Aggregators: FiveThirtyEight - FiveThirtyEight is a news and sports site with data-driven articles. Thank you. Blue points represent people that the model inferred are high income and red points represent those whom the model inferred are low income. 0. Also, the tool can break down model performance by subsets of the data and look at fairness metrics between those subsets. Feature selection, the process of finding and selecting the most useful features in a dataset, is a crucial step of the machine learning pipeline. clear. There are around 350 datasets in the repository, categorized by things like task, attribute type, data type, area, or number of attributes or instances. The overall accuracy at this threshold is lower than at the previous threshold, but the number of false positives plummeted, as is desired with this cost ratio setting. Binary Classification Model: UCI Census Income Prediction. We say this might be a good setting, as that threshold setting should be verified over a larger test set if available, and there may be other factors to consider, such as fairness (which we will get into soon). With the default cost ratio of 1, if we click "optimize threshold" then the positive classification threshold changes to 0.4. In notebooks, WIT can also be used on models served through Cloud AI Platform Prediction, through the set_ai_platform_model method, or with any model you can query from python through the set_custom_predict_fn method. Sun and Wong (Sun and Wong 2010) offer several suggestions dependent on the context of the problem. This might sound simple and we might be hoping that an aggregate function is already available. When we press this button, the tool will take the cost ratio into account, and come up with ideal separate thresholds for men and women that will achieve demographic parity over the test dataset. WIT can be used inside a Jupyter or Colab notebook, or inside the TensorBoard web application. This means more true positives and false positives, and less true negatives and false negatives. Today we're gonna talk about clustering and mixture models Also, the current threshold point on the ROC curve moves up and to the right, meaning a higher true positive rate and higher false positive rate, as the model becomes more permissive in who it deems as high income. Does a cryptographic oracle have to be a server? By default, WIT uses a positive classification threshold of 0.5. Does a meteor's direction change between country or latitude? I also wanted to share with others how I went about the technical aspects of my exploration. Another way to show the relationship between feature values and model scores is to look at global partial dependence plots. Understanding biases in your datasets and data slices on which your model has disparate performance are very important parts of analyzing a model for fairness. The flat, semi-transparent line shows the current positive classification threshold being used, so points on the dark blue line above that threshold represent where the model would label someone as high-income. While the possibilities are endless, here is a small list of visualizations that you may find interesting with this model. You have to iterate again the dataset and, for each line, show only those who are int the most common data set. WIT has buttons to optimize for other fairness constraints as well, such as "equal opportunity" and "equal accuracy". Because it is the nearest counterfactual, is shows up as the left-most blue point in the visualization. 1 2 Upon clicking the "partial dependence plots" button in the right-side controls, we immediately see the plot for the selected datapoint if the age of this person is changed from a minimum of 17 to a maximum of 72. What do you roll to sleep in a hidden spot? the inference label". Set the binning of X-axis by marital status, scattering of X-axis by age, scattering of Y-axis by inference score and color by inference label. First, the "Features" tab shows an overview of the provided dataset, using a visualization called Facets Overview. If the input lines are sorted, you may just do a set intersection and print those in sorted order. GitHub is where people build software. Active guard shielding for instrumentation amplifier. Our team maintains Gnip-Tweet-Evaluation a repository on GitHub that contains some tools to do quick evaluation of a Tweet corpus (that package is useful as a command line … Making statements based on opinion; back them up with references or personal experience. Why is non-relativistic quantum mechanics used in nuclear physics? For numeric features, capital gain is very non-uniform, with most datapoints having it set to 0, but a small number having non-zero capital gains, all the way up to a maximum of 100,000. We can now see the specifics of the datapoint we clicked on, including its feature values and its inference results. 2 has 2 occurrences, We think that WIT provides a great interface for furthering ML fairness learning, but of course there is no silver bullet to improving ML fairness. Above: A scatterplot of distance from a datapoint of interest versus model prediction score. Machine learning fairness is an active and important area of research. most_common() will return the pairings in order of most common to least, of you want the first n most common just pass n d.most_common(n) Share Improve this answer Let's add some more data to this chart. The prediction task is to determine whether a person is high income (defined as earning more than $50k a year). Extraction was done by Barry Becker from the 1994 Census database. We now see two datapoints being compared side by side. We can also see how similar every point in the dataset is to our selected datapoint through the "show similarity to selected datapoint" button. It seems that the model has learned a positive correlation between age and income, which makes sense as people tend to earn more money as they grow older. Fortunately, some publications have started releasing the datasets they use in their articles. Models. By exploring a novel dataset with several (more than 10) features and many instances (more than 10,000), I was hoping to conduct a predictive exercise that could show a bit more of a challenge. For example, setting this to "sex" allows us to see the breakdown of model performance on male datapoints versus female datapoints. Above: The aggregate performance of this model after we lower the positive classification threshold. Understanding the behavior of C's preprocessor when a macro indirectly expands itself, What would justify those road like structures. Fill out the path to the TFRecords file, the host/port of the running TensorFlow Model Server, and the name of the model on that model server that you wish to test. We now see a scatterplot of education level versus inference score. Because of the vast difference in the properties of the male and female training data in this 1994 census dataset, we need quite different thresholds to achieve demographic parity. Essentially it is the most common value in a given set of data. Above: The partial dependence plot of the age feature for a selected datapoint. This specific dataset and prediction task combination is often used in machine learning modeling and fairness research, partially due to the dataset's understandable attributes – including some containing sensitive classes such as age and gender, and partially due to a clear, real-world prediction task. If we change the cost ratio to 2 and click the optimize threshold button, the optimal threshold moves up to 0.77. Explore the basics of the What-If Tool through a specific example. Set the binning of the X-axis to hours-per-week. 15 16 17 18 19 20. Application of Python functions to parse data, apply IF statements, and create a dictionary in order to calculate the frequency of different weather conditions in Los Angeles. Women and minorities seem under-represented in this dataset. Models for language identification and various supervised tasks. The details of the datapoint should appear in the datapoint editor panel to the left of the visualization. Now our selected datapoint, highlighted in yellow, is the left-most datapoint as it is completely similar to itself. The points on the extreme right are the most dissimilar from the selected datapoint. If they were all tested already, then the current node must be a leaf with a label that matches the most common value of the target attribute (label) in the dataset. In this case, we would want a low cost ratio, as we prefer false positives to false negatives. The use of these features can help shed light on subsets of your data on which your classifier is performing very differently. In general, what can I learn through use of the What-If Tool? Normal use of WIT within TensorBoard requires your model to be served through a TensorFlow Model Server, and the data to be analyze must be available on disk as a TFRecords file. This is a much deeper question still falling under the ML fairness umbrella and worthy of discussion outside of WIT). For categorical features, country is the most non-uniform with most datapoints being from the USA, but there is a long tail of 40 other countries which are not well represented. Each partial dependence plot shows how the model's positive classification score changes as a single feature is adjusted in the datapoint. Notice that now there is a second "run" of results in the inference results section, in which the positive class score was 0.510. 7.4 Class comparison maps. To use WIT inside a notebook, create a WitConfigBuilder object that specifies the data and model to be analyzed. Now let's explore how the trained model performs on 5,000 records from a test set that we held out. Then, provide this object to the WitWidget object. Datasets for Current Events. According to the documentation, c.most-common returns a list of tuples, you can get the desired output as follow: Thanks for contributing an answer to Stack Overflow! High capital gains is a very strong indicator of high income, much more than any other single feature. Cluster analysis is a set of tools for looking at data and . Above: A datapoint and its nearest counterfactual. The mode An average found by determining the most frequent value in a group of values. In this case, our simple linear model can be about 82% accurate over the dataset with the optimal threshold. The default cost ratio in the tool is 1, meaning false negatives and false positives are equally undesirable. I want to be able to generate a new dataset just with the most frequent items, in this case the 4 most common: I am finding the first 50 most common items, but I am failing to print them out in a correct way. Above: The optimal classification threshold when using a cost ratio of 1.0. There are many approaches to improving fairness, including augmenting training data, building fairness-related loss functions into your model training procedure, and post-training inference adjustments like those seen in WIT. What is the difference between Python's list methods append and extend? If set to FALSE, query the API for the data, and refresh the temporary cache with the result. Set the binning of the X-axis to age, the binning of the Y-axis to marital status, and color by inference label. This developer built a…. Asking for help, clarification, or responding to other answers. Data mining is the process of finding patterns in large datasets. Imagine a scenario where this simple income classifier was used to approve or reject loan applications (not a realistic example but it illustrates the point). In statistics, mode is defined as the value that appears most often in a set of data. 1 means that the model is entirely confident that the datapoint falls in the high income category, and 0 means that it is not at all confident. There are countless ways that a dataset can be biased, leading to models trained from that dataset affecting different populations differently (such as a model giving less loans to women than men because it is based on historical, outdated data showing less women in the workplace). The confusion matrix shows that as the threshold is lowered, the model considers more and more datapoints as being high income (at a threshold value of 0.25, if the model predicts the positive class with a score of 0.25 or more, than the point is considered as being high income). Above: The aggreate performance of this model on our test data. In this case, we would want a high cost ratio, as we prefer false negatives to false positives. 0. The most common method for calculating correlation is Pearson’s Correlation Coefficient, that assumes a normal distribution of the attributes involved. 1 2 3 4 5 6 7 Partial dependence plots allow a principled approach to exploring how changes to a datapoint affect the model's prediction. Color by marital status. 4 has 4 occurrences, In this walkthrough, we explore how the What-If Tool (WIT) can help us learn about a model and dataset. See this page for an explanation of the difference between these two distance measurements. site design / logo © 2021 Stack Exchange Inc; user contributions licensed under cc by-sa. Using a training set of ~30,000 records, we've trained a simple linear classifier for this binary classification task. We want to hear from you! These datasets provide the aggregated tax, SNAP benefits, and poverty universe data used in producing the SAIPE estimates. If we wished to ensure than men and women get their loans approved the same percentage of the time, that is a fairness concept called "demographic parity". For our selected datapoint, which was inferred as low income, the nearest counterfactual is the most similar person which the model inferred as high income. Click "Accept". Predict whether income exceeds $50K/yr based on census data A dataset is a file for public use to download for analysis in spreadsheet, statistical, or geographic information systems software. Discovering groups, species, or categories, Defining boundaries between groups. In our case, that feature in the dataset is named "Over-50K", so we set the ground truth feature dropdown to the "Over-50K" feature. In our dataset, this is described as a number that represents the last school year that a person completed. index (target)] for record in data] default = majority (attributes, data, target) # If the dataset is empty or the attributes list is empty, return the # default value. Let's change our X-axis scatter to show the L1 distance to the selected datapoint. This notebook demonstrates WIT on a smile detection classifier. I figured I would play around with the data, to at least learn some new skills, and at most find something interesting in the dataset. In this case, demographic parity can be found with both groups getting loans 16% of the time by having the male threshold at 0.78 and the female threshold at 0.12. Optimizing for a cost ratio of 1 is the same as optimizing for accuracy, as it will minimize the total number of false positives and false negatives. Correlation Between Attributes. The features in this visualization can be sorted by a number of different metrics, including non-uniformity.
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