Zhang et al. Generally, most modern ML applications that involve text data are based on rather sophisticated natural language models. Fit trains the imputation model on given data while cross-validating a set of hyperparameters, and transform allows imputing missing values of the to-be-imputed column the imputation model is trained on. Yes, they do - and in the real world, these missing values can be divided into three categories. In doing so, this package brings missing data imputation methods to the Python world and makes them work nicely in Python machine learning projects (and specifically ones that utilize scikit-learn). In this study, we developed an experimental protocol and conducted a comprehensive benchmark for imputation methods comparing classical and modern approaches on a large number of datasets under realistic missingness conditions with respect to the imputation quality and the impact on the predictive performance of a downstream ML model. Determine whether the missing values are random or systematic, as this can influence the appropriate handling technique. When training data are fully observed, our results demonstrate that, in more than 75% of our experiments, imputation leads to improvements in downstream ML model predictive performance between 10% and 20% for classification tasks and around 15% for regression tasks. Benchmark for Predictive Models. Kingma, D. P., and Welling, M. (2014). sample_posterior=True. (2001). This fact, combined with our large number of experimental conditions (see Table 6), results in vast computational costs. We summarize the abovementioned articles and related benchmarks in Table 1. For categorical columns, generally, the ranks show higher variance. (2020). For a certain missingness pattern and fraction, e.g., 30% MAR, we introduce 30%N missing values of this pattern to each of the N columns. In May 2023, Frontiers adopted a new reporting platform to be Counter 5 compliant, in line with industry standards. Pattern Recognition 107, 107501. doi:10.1016/j.patcog.2020.107501. Missing But all in all, our results suggest that high-capacity deep learning models are not better than conventional methods like random forests and generative models are not consistently better than discriminative models. 4). Poulos and Valle (2018) compared the downstream task performance on two binary classification datasets (N=48,842, and N=435) with imputed and incomplete data. For each of the datasets, we sample one to-be-imputed column upfront, which remains static throughout our experiments. The calculation of the imputation quality (Experiment 1, Section 4.1.1) remains the same. 3. Nazbal, A., Olmos, P. M., Ghahramani, Z., and Valera, I. To summarize, the best performing imputation approach is random forest. For various reasons, many real world datasets contain missing values, often This effect is largely independent of whether the imputation methods are trained on complete or incomplete data. With this experiment, we aim to reveal how accurately the imputation methods can impute the original values. In recent years, complex data pipelines have become a central component of many software systems. fashion: at each step, a feature column is designated as output y and the You just let the algorithm handle the missing data. However, this comes at the price of losing data which may be valuable (even though incomplete). Today we'll explore one simple but highly effective way to impute missing data the KNN algorithm. doi:10.1145/2641190.2641198. Intelligence 32, 186196. least one neighbor with a defined distance, the weighted or unweighted average Discov. This in dummy form will be 2 columns.. male and female, with a binary 0 or 1 instead of text. In the following, we use the terms F1-score and F1 synonymously for macroF1-score. To visualize the data and results, both Python (matplotlib, version 3.0.0 . array are numerical, and that all have and hold meaning. it repeatedly to the same dataset with different random seeds when doi:10.1145/3269206.3272005, Bse, J.-H., Flunkert, V., Gasthaus, J., Januschowski, T., Lange, D., Salinas, D., et al. (2016). Adding boolean value to indicate the observation has missing data or not. Yet, it remains hard to assess which imputation method consistently performs best in a large spectrum of application scenarios and datasets under realistic missingness conditions. These allow us to get a decent impression of the distribution of the results based on quantiles. Automated Machine Learning - Methods, Systems, Challenges (Cham, Switzerland: The Springer Series on Challenges in Machine Learning (Springer). (2018) show that simple deep learning models can achieve good imputation results. The overall goal of an imputation method is to train a model on a dataset Xnd=[x1,x2,,xi1,xi+1,,xd], where d is the number of features, n is the number of observations, and xi denotes the to-be-imputed column. Data Part, 7th International Conference on Learning Representations, ICLR 2019, Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 6th International Conference on Learning Representations, ICLR 2018, Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, Proceedings of the 24th International Conference on Extending Database Technology, EDBT 2021, Proc. This metric is labeled Improvement and represented on the plots y-axis. 2020 ACM SIGMOD Int. For discriminative imputation approaches, we substitute missing values with their column-wise mean/mode value, one-hot encode categorical columns, and normalize the data to zero mean and unit variance. Proc. For training, we use Adam optimizer with default hyperparameters, batch size of 64, and early stopping within 50 epochs. dataset. classification). Conf. (2018). Second, we find that, in almost all experiments, random-forestbased imputation achieves the best imputation quality and the best improvements on the downstream predictive performance. Statistical Analysis Spectral Normalization for Generative Adversarial Networks, in 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018. Why do we need to impute missing data values? We filter available datasets as follows. Bertsimas, D., Pawlowski, C., and Zhuo, Y. D. (2017). GAIN shows in most settings consistently the worst performance: rank four or worse in 75% of the cases. This allows us to interpret the results relative to each other. Thus, the imputation and downstream ML model has to be trained on incomplete training data. However, they show that the combination of the imputation method, downstream model, and metric (F1 or AUC) influences the results. 4) Imputation methods and optimized hyperparameters: We use six imputation methods that range from simple baselines to modern deep generative models. errors) allow the data scientist to obtain understanding of how analytic Filling the Missing Values - Imputation 5. n_neighbors and there are no defined distances to the training set, the Learn. Some estimators are designed to handle NaN values without preprocessing. doi:10.1145/3398730.3399194, Yin, P., Neubig, G., Yih, W.-t., and Riedel, S. (2020). This effect also holds for the missingness fractions: the higher the missingness fraction, the higher the potential improvements. feature matrix. We then removed duplicated, corrupted, and Sparse ARFF2 formatted datasets. We conclude that when training data are fully observed, an imputation model should be trained along with the downstream ML model to improve data quality problems in the data ingested at inference time by a downstream ML component. Filling with a Regression Model Conclusion Frequently Asked Questions Why Fill in the Missing Data? The imputation aims to assign missing values a value from the data set. Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S. (2017). Each imputation method is evaluated regarding the imputation quality and the impact imputation has on a downstream ML task. "Deep" Learning for Missing Value Imputationin Tables with Non-numerical Data, . Editors I. Guyon, U. von Luxburg, S. Bengio, H. M. Wallach, R. Fergus, S. V. N. Vishwanathan, and R. Garnett, 66266637. 2, 25032511. ecosystem: Amelia, mi, mice, missForest, etc. missing values. Intelligence 33, 913933. The wall-clock run time is measured in seconds when calling our frameworks fit and transform methods (see Section 4 for details), which means that the training duration incorporates hyperparameter optimization (see Section 3.5 for details). These data can be found here: https://www.openml.org. In the most challenging MNAR condition, mean/mode imputation achieves competitive results. Copyright 2021 Jger, Allhorn and Biemann. To compensate for the disadvantage of a single imputation methodwhere missing values are replaced with a single valuethe multiple imputation method generates several data sets and the results are combined into a single result to replace the missing values. 'constant' strategy: A more sophisticated approach is to use the IterativeImputer class, Responsible Data Management. NORMAL IMPUTATION In our example data, we have an f1 feature that has missing values. doi:10.1080/08839514.2019.1637138. Learn. Lectures Data Manag. See [2], chapter 4 for more discussion on multiple DecisionTreeClassifier) to be able to make predictions. Filling missing values a.k.a imputation is a well-studied topic in computer science and statistics. Computer 42, 3037. i-th feature dimension using only non-missing values in that feature dimension Analyzing the type of missingness in your dataset is a very important step towards treating missing values. Journal of Statistical Software 45: Appl. Salimans, T., Goodfellow, I. J., Zaremba, W., Cheung, V., Radford, A., and Chen, X. On the other hand, once found, the hyperparameters for generative models influence the inference time less than for k-NN or random forest, whose prediction times depend heavily on the hyperparameters. VLDB Endow. For categorical columns (see Figures 1, 2, upper row) in the more challenging imputation settings MAR or MNAR with large missingness fractions, the mean/mode imputation tends to achieve better ranks. Probabilistic Demand Forecasting at Scale. For instance, one can use crowdsourced tasks to collect all necessary features in the training data or use sampling schemes that ensure complete and representative training data. doi:10.1080/08839514.2018.1448143, Qiu, Y. L., Zheng, H., and Gevaert, O. Other comparisons show a slight advantage of discriminative deep learning methods over random forests (Biessmann et al., 2019), but these experiments were conducted on a much smaller selection of datasets. FAccT '21: 2021 ACM Conference on Fairness, Accountability, and Transparency, Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, The Springer Series on Challenges in Machine Learning (Springer, Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems, Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2nd International Conference on Learning Representations, ICLR 2014, Proc. We use the default task settings of jenga in which scikit-learns SGDClassifier is used for classification and SGDRegressor for regression tasks. Since we compare six imputation methods, the possible imputation ranks range between 1 and 6. Another line of imputation research in the statistics community focuses on multiple imputation (MI) (Rubin, 1987). imputers in a more complex machine-learning pipeline. In our experiments, we apply the following three preprocessing steps for all the imputation methods: Encode categorical columns: Categories are transformed into a numerical representation, which is defined on the training set and equally applied to the test set, Replace missing values: To avoid the imputation model from failing, Normalize the data: The columns are rescaled to the same range, which is defined on the training set and equally applied to the test set. (20172017). (2021). There isn't always one best way to fill missing values in fact. They conclude that using a k-NN imputation model performs best in most situations. (e.g. Imputation ranks of the imputation methods trained on complete data. The results of our experiments are described and visualized in Section 5. Conference Track Proceedings (OpenReview.net). In all settings, there are hardly any improvements greater than 1%. Manag. Although they are all useful in one way or another, in this post, we will focus on 6 major imputation techniques available in sklearn: mean, median, mode, arbitrary, KNN, adding a missing indicator. (2020). The imputation methods k-NN and random forest rank best with a tendency of random forest to outperform k-NN, where random forests variance is higher. y) for known y. algorithms use the entire set of available feature dimensions to estimate the doi:10.14778/3415478.3415570, Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., et al. When imputation methods were trained on incomplete data, the positive impact of imputing missing values in the test data was substantially lower, sometimes even negative. package (Multivariate Imputation by Chained Equations) [1], but differs from Additionally, it adds the indicator variables from Most benchmarks use broad missingness fractions but lack realistic missingness conditions or a large number of heterogeneous datasets. The probability of the missing data is entirely random and is not dependent on already observed data, i.e., P ( M i s s i n g | C o m p l e t e d a t a) = p ( M i s s i n g). (2016); Heusel et al. We focus on a comprehensive evaluation with several numeric datasets and tasks (regression, binary classification, and multiclass classification). 1- Mean Imputation: the missing value is replaced for the mean of all data formed within a specific cell or class. values. We use Scenario 1 to simulate such situations and run both experiments, as described in Section 4.1.1 and Section 4.1.2. Creative Commons Attribution License (CC BY). Jadhav, A., Pramod, D., and Ramanathan, K. (2019). with a constant values. Pandas provides a flexible and efficient way to . Approach 3: Impute the missing data, that is, fill in the missing values with appropriate values. While some imputation methods are deemed appropriate for a specific type of data, e.g. For regression tasks and imputing numerical columns, we use the RMSE. However, in that work, the authors only considered text data as an input field to an imputation method, not as a column that could be imputed. Process. Tavares and Soares [2018] compare some other techniques with mean and conclude that mean is not a good idea. J. Mach. Learning to Validate the Predictions of Black Box Classifiers on Unseen Data, in Proc. The evaluation of the imputation quality is then performed using the to-be-imputed columns discarded values as ground truth and the imputation models predictions. Overview of our experimental settings. This effect also holds for their potential improvement (75% quantile), except for 50% MNAR, where it is about five percentage points higher than the others. Stef van Buuren, Karin Groothuis-Oudshoorn (2011). As described in Section 4.1.2, this time, we discard only values in the datasets randomly sampled target column. This estimator is still experimental for now: default parameters or However, optimizing the hyperparameters for all datasets is out of the scope of this article. Finally, to train a robust model, it 5-fold cross validates the hyperparameters loss, penalty, and alpha using grid search. (2021)). In a single imputation method the missing data are filled by some means and the resulting completed data set is used for inference. In the MAR condition, we discard values if values in a random other column fall in that percentile. For categorical columns, we use autokeras StructuredDataClassifier and for numerical columns StructuredDataRegressor. An overview of all imputation methods and their hyperparameters we optimized. Reasons for incomplete data are manifold: data might be accidentally not recorded, lost through application or transmission errors, intentionally not filled in by users, or result from data integration errors. Impact on the downstream task of the six imputation methods trained on incomplete data. encoded as np.nan, using the mean value of the columns (axis 0) Proc. However, incorporating the models training and inference time, presented in Table 7, shows that the discriminative DL approach is substantially slower for training and inference than the other two methods. Most importantly, no paper systematically compares imputation methods trained on complete and incomplete datasets. Understanding these categories will give you with some insights into how to approach the missing value (s) in your dataset. However, because we plan to run many experiments, the datasets must not be too big to keep training times feasible. (2016). (2010), can be thought of as examples of generative models for imputation. Its input is the concatenation of the generators output and a hint matrix, which reveals partial information about the missingness of the original data. The results of the final An Analysis of Four Missing Data Treatment Methods for Supervised Learning. Editors Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K. Q. Weinberger (Montral, Canada: Curran Associates, Inc.), 27, 26722680. doi:10.1080/713827181, Baylor, D., Breck, E., Cheng, H.-T., Fiedel, N., Foo, C. Y., Haque, Z., Haykal, S., Ispir, M., Jain, V., Koc, L., Koo, C. Y., Lew, L., Mewald, C., Modi, A. N., Polyzotis, N., Ramesh, S., Roy, S., Whang, S. E., Wicke, M., Wilkiewicz, J., Zhang, X., and Zinkevich, M. (2017). This is because we used the expensive default model optimization of AutoKeras. In the statistics community, it is common practice to perform multiple Few studies report results on the more challenging conditions MAR and MNAR. Imputation methods based on this type of generative model include those in the work of Nazbal et al. We first introduce missing values in the training and test set and then train the baseline and imputation models based on these incomplete data. Moreover, poor data quality can foster unfair automated decisions, which marginalize minorities or have other negative societal impacts (Stoyanovich et al., 2020; Yang et al., 2020; Bender et al., 2021). The Python package scikit-learn (Pedregosa et al., 2011) can use this API to download datasets and create well-formatted DataFrames that encode the data properly. All authors wrote sections of the manuscript and contributed to its revision and read and approved the submitted version. 9, 9931004. Tabert: Pretraining for Joint Understanding of Textual and Tabular Data, in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, July 5-10, 2020. Most research on missing value imputation considers three different types of missingness patterns: Missing completely at random (MCAR, see Table 2): Values are discarded independently of any other values, Missing at random (MAR, see Table 3): Values in column c are discarded depending on values in another column kc, Missing not at random (MNAR, see Table 4) Values in column c are discarded depending on their value in c. TABLE 2. The m final analysis results (e.g. Discov. The authors show that the proposed method outperforms the baselines, closely followed by k-NN and iterative k-NN. constructed. Psychol. The potential improvements when the imputation methods are trained on incomplete data are marginal. SD separately for Training and Inference. When imputing numerical columns, the differences are more pronounced. Our results demonstrate that, especially in the challenging scenarios where a large fraction of values is missing, there is a high variance in the imputation performance metrics. Editors J. Nie, Z. Obradovic, T. Suzumura, R. Ghosh, R. Nambiar, C. Wang, H. Zang, R. Baeza-Yates, X. Hu, J. Kepner, A. Cuzzocrea, J. Tang, and M. Toyoda (IEEE Computer Society), 766775. Among the categories are: When data are missing completely at random (MCAR), the probability of any particular value being missing from your dataset is unrelated to anything else. values in the matrix because it would densify it at transform time. 2015-Janua. Second, we average those values for each imputation method and present them in Table 7. Exploring fewer hyperparameters could decrease its imputation performance drastically. Therefore, we decide to define the hyperparameter grids once and incorporate the imputation methods robustness regarding their hyperparameters into our evaluation. However, comparing imputation methods with respect to the calibration of their uncertainty estimates is an important topic for future research and could be conducted with the same experimental protocol that we developed for our point estimate comparisons. For MCAR with 50% missing values and MAR with 10% to 50% missingness, the k-NN imputation approach performs well and gets for 75% of the cases at least rank three or better.
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