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Print Opinion Column: The Jan. The committee was established against the backdrop of vast gaps in our knowledge about the Capitol attack. It is a national imperative - one that all public leaders should share - to get to the bottom of what happened when thousands of rioters attempted to scuttle a fair election, threatening American democracy itself.

An earlier version of this article misstated the name of the chairman of the Jan. It is Bennie Thompson, not Bennie Johnson. Nearly the entire Republican Party attempted to scuttle any inquiry into Jan.

The only two GOP committee members, anti-Trumpers Rep. In its first public hearing in late July, select committee Chairman Rep. Thousands of pages from government archives were turned over Sept. The congressman bobbed and weaved like a boxer on the ropes. I spoke with him on Jan. Before, during or after the attack.

Quoted in Politico, he copped to more than one call with Trump on Jan. Why the sudden clarity. Maybe because Jordan realizes he will probably be called to testify, under oath, before the committee and the country.

Any discrepancy between his public statements and hard evidence - like phone company records - could prove humiliating, not to mention politically dangerous. It might even put him in criminal jeopardy. This force of legal process is exactly what the country was denied during the Trump era, when the president and his enablers brazenly lied, stonewalled and doubled down to evade document requests and testifying under oath.

Trump relied on throwing sand into the gears of justice and government bureaucracy, maneuvering to keep his behavior sealed from scrutiny. Individuals may still try to stonewall, but Thompson and the other members show every intention to push past the maneuverings of Republicans, including Minority Leader Kevin McCarthy (R-Bakersfield), another Congress member likely to be called as witness.

In fact, the committee could achieve a major breakthrough. Americans of all political stripes should champion the effort. A full accounting of Jan. Harry Litman, the legal affairs columnist for the Opinion page, is a former U. Litman teaches constitutional law and national security law at the University of California and practices law at Constantine Cannon in San Francisco.

Police officers form a line on Lemoyne Street to block access to a homeless camp in Echo Park that was closed on Thursday, Mar. Dozens of homeless people lived at the park, but the number had dwindled to about a dozen after authorities told camp residents they had 24 hours to leave. For the record: 7:28 a. Subscribers Are ReadingCalifornia In red California, anger over recall blowout. Activism is Editorial: Dump COVID vaccine religious exemptions.

Tweet Share Share Last Updated on August 20, 2020Feature selection is the process of reducing the number of input variables when developing a predictive model. It is desirable to reduce the number of input variables to both reduce the computational cost of modeling and, in some cases, to improve the performance of the model.

Statistical-based feature selection methods involve evaluating the relationship between each input variable and the target variable using statistics and selecting those input variables that have the strongest relationship with the target variable. These methods can be fast and effective, although the choice of statistical measures depends on the data type of both the input and output variables.

As such, it can be challenging for a machine learning practitioner to select an appropriate statistical measure for a dataset when performing filter-based feature selection. In this post, you will discover how to choose statistical measures for filter-based feature selection with numerical and categorical data. Kick-start your project with my new book Data Preparation for Machine Learning, including step-by-step tutorials and the Python source code files for all examples.

How to Develop a Probabilistic Model of Breast Cancer Patient SurvivalPhoto by Tanja-Milfoil, some rights reserved. Feature selection methods are intended to reduce the number of input variables to those that are believed to be most useful to a model in order to predict the target variable. Feature selection is primarily focused on removing non-informative or redundant predictors from the model.

Some predictive modeling problems have a large number of variables that can slow the development and training of models and require a large amount of system memory.