finding most frequent attributes set in census dataset github
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! JavaScript MIT 126 14 0 0 Updated Jan 6, 2015 In this case, the nearest counterfactual is slightly older and has a different occupation, but is otherwise identical. The confusion matrix shows, for the currently-set classification threshold, how many true positives, true negatives, false positives, and false negatives the model predicted over the dataset. This might sound simple and we might be hoping that an aggregate function is already available. Above: The dialog for using similiarity in the datapoints visualization. Then, provide this object to the WitWidget object. If set to FALSE, query the API for the data, and refresh the temporary cache with the result. 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. These scores are very close to the decision threshold of 0.5 that the tool initially uses. Above: Using WIT in a notebook with a TF Estimator. 8 9 10 11 12 13 14 Github: facebookresearch/fastText. Above: The edited datapoint causes a change in prediction. What is the mathematical meaning of the plus sign (+) in chemical reaction equations? 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. Each partial dependence plot shows how the model's positive classification score changes as a single feature is adjusted in the datapoint. Now our selected datapoint, highlighted in yellow, is the left-most datapoint as it is completely similar to itself. Above: The initial scatterplot of results. 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. 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. Having a good set of descriptive statistics coded up that you always run on a new dataset can be helpful. In statistics, mode is defined as the value that appears most often in a set of data. This includes information such as age, marital status and education level. Also known as "Census Income" dataset. The mrc dataset contains information on Québec regional county municipalities (MRCs) in a ESRI shapefile format. When during construction of them, did Bible-era Jewish temples become "holy"? Does C++ guarantee identical binary layout for "trivial" structs with a single trivial member? In this case, 28% of men from the test dataset have their loans approved but only 10% of women have theirs approved. By simply changing the age of this person, the model now predicts that they are high income. Above: Finding demographic parity in the Performance & Fairness tab. expand_more. We can see that the model is more accurate (has less false positives and false negatives) on females than males. Short story about a psychically-linked community with a collective delusion. Models. 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. Clicking on a datapoint highlights it in the visualization. 0 Active Events. Also, the tool can break down model performance by subsets of the data and look at fairness metrics between those subsets. There is no single solution to effectively convey both estimates and associated uncertainty in a map. Above: A scatterplot of distance from a datapoint of interest versus model prediction score. By default, WIT uses a positive classification threshold of 0.5. Additionally, the points are laid out top to bottom by a score for how confident the model is that the person is high income, called "inference score". 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. WIT can help investigate fairness concerns in a few different ways. Join Stack Overflow to learn, share knowledge, and build your career. 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". The mode An average found by determining the most frequent value in a group of values. 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. In this case, our simple linear model can be about 82% accurate over the dataset with the optimal threshold. Does a cryptographic oracle have to be a server? Thanks for checking out this walkthrough of the What-If Tool on the UCI census binary classification task. Recent state-of-the-art English word vectors. 1 2 3 4 5 6 7 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. By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy. The UCI Census dataset is a dataset in which each record represents a person. I am getting a list of tuples for p, I tried to convert it to a dict and then say : Finding the most frequent items in a dataset, State of the Stack: a new quarterly update on community and product, Podcast 320: Covid vaccine websites are frustrating. WIT can be used inside a Jupyter or Colab notebook, or inside the TensorBoard web application. The use of these features can help shed light on subsets of your data on which your classifier is performing very differently. 7.4 Class comparison maps. Partial dependence plots allow a principled approach to exploring how changes to a datapoint affect the model's prediction. Back on the initial view, let's further investigate a datapoint near the decision boundary, where datapoints turn from red to blue. There are around 350 datasets in the repository, categorized by things like task, attribute type, data type, area, or number of attributes or instances. Notice how with the high male threshold there are many more false negatives than before, and with the low female threshold there are many more false positives than before. It is a form of "unsupervised" learning, which means that the only input is the dataset itself; the algorithm is not given any correct examples to learn from. Blue points represent people that the model inferred are high income and red points represent those whom the model inferred are low income. We now see two datapoints being compared side by side. 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. The features in this visualization can be sorted by a number of different metrics, including non-uniformity. 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). 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. Navigate to the web address of your TensorBoard instance, select "What-If Tool" from TensorBoard's dashboard selector. As the plot shows, as age increases, the model believes more confidently that this person is high income. In this walkthrough, we explore how the What-If Tool (WIT) can help us learn about a model and dataset. To learn more, see our tips on writing great answers. A dataset is a file for public use to download for analysis in spreadsheet, statistical, or geographic information systems software. If the input lines are sorted, you may just do a set intersection and print those in sorted order. In this case, we would want a low cost ratio, as we prefer false positives to false negatives. 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. We will show later in this tutorial how to change this threshold. Finding datasets for current events can be tricky. Why don't we see the Milky Way out the windows in Star Trek? Is it a bad sign that a rejection email does not include an invitation to apply again in the future? 1 has 2 occurrences, 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. These datasets provide the aggregated tax, SNAP benefits, and poverty universe data used in producing the SAIPE estimates. 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. clear. 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: Now we can see a positive classification threshold slider, confusion matrix, and ROC curve for the model. Sometimes the data may come from a source that contains biases, for instance, human-labeled data that reflects the biases of the humans. 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). Correlation Between Attributes. 4 high income. How to print colored text to the terminal? 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. Remove the scatterplot settings by using "(default)". A cost ratio of 3 means that we consider a false positive 3 times as costly as a false negative. But in a system that unlocks a front door given a face identification match, it is more important to avoid false positives, as the expense of sometimes not incorrectly automatically opening the door for a complete stranger. Another way to show the relationship between feature values and model scores is to look at global partial dependence plots. For this datapoint, the inference score for the positive (high income) class was 0.472, and the score for negative (low income) class was 0.528. Models for language identification and various supervised tasks. The What-If Tool is being actively developed and documentation is likely to change as we improve the tool. auto_awesome_motion. High capital gains is a very strong indicator of high income, much more than any other single feature. the inference label". Above: Setup dialog for WIT in TensorBoard. Is US Congressional spending “borrowing” money in the name of the public? This developer built a…. 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. Word vectors for 157 languages trained on Wikipedia and Crawl. Why is non-relativistic quantum mechanics used in nuclear physics? We can now see the specifics of the datapoint we clicked on, including its feature values and its inference results. We can see that of the 5,000 test datapoints, over 3,300 are from men and over 4,200 are from caucasions. The green text represents features where the two datapoints differ. How to center vertically small (tiny) equation numbered tags? Notice that now there is a second "run" of results in the inference results section, in which the positive class score was 0.510. Attribute Information: Listing of attributes: >50K, =50K. In the case of the exam scores, the mode of the array is 75 as this was received by the most … The default cost ratio in the tool is 1, meaning false negatives and false positives are equally undesirable. What do you roll to sleep in a hidden spot? Set the binning of the X-axis to hours-per-week. We can also see how similar every point in the dataset is to our selected datapoint through the "show similarity to selected datapoint" button. The nearest counterfactual is the most similar datapoint that has a different inference results or in our case, a different classification. Above: A histogram of ages, with datapoints colored by marital status. What can we learn from this initial view? So the model is clearly learning that there is a positive correlation between education level and being high income.