What is true negative?

True Negative (TN): A true positive is an outcome where the model correctly predicts the positive class. Similarly, a true negative is an outcome where the model correctly predicts the negative class. A false positive is an outcome where the model incorrectly predicts the positive class.

developers.google.com - True vs. False and Positive vs. Negative | Machine Learning

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Make Your Bookie Cry And Pull The Hair By Generating $23,187

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Estimated Time: 5 minutes

In this section, we'll define the primary building blocks of the metrics we'll use to evaluate classification models. But first, a fable: An Aesop's Fable: The Boy Who Cried Wolf (compressed) A shepherd boy gets bored tending the town's flock. To have some fun, he cries out, "Wolf!" even though no wolf is in sight. The villagers run to protect the flock, but then get really mad when they realize the boy was playing a joke on them. [Iterate previous paragraph N times.] One night, the shepherd boy sees a real wolf approaching the flock and calls out, "Wolf!" The villagers refuse to be fooled again and stay in their houses. The hungry wolf turns the flock into lamb chops. The town goes hungry. Panic ensues.

Let's make the following definitions:

"Wolf" is a positive class .

. "No wolf" is a negative class.

We can summarize our "wolf-prediction" model using a 2x2 confusion matrix that depicts all four possible outcomes:

True Positive (TP): Reality: A wolf threatened.

Shepherd said: "Wolf."

Outcome: Shepherd is a hero. False Positive (FP): Reality: No wolf threatened.

Shepherd said: "Wolf."

Outcome: Villagers are angry at shepherd for waking them up. False Negative (FN): Reality: A wolf threatened.

Shepherd said: "No wolf."

Outcome: The wolf ate all the sheep. True Negative (TN): Reality: No wolf threatened.

Shepherd said: "No wolf."

Outcome: Everyone is fine.

A true positive is an outcome where the model correctly predicts the positive class. Similarly, a true negative is an outcome where the model correctly predicts the negative class. A false positive is an outcome where the model incorrectly predicts the positive class. And a false negative is an outcome where the model incorrectly predicts the negative class. In the following sections, we'll look at how to evaluate classification models using metrics derived from these four outcomes.

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What happens if you day trade too much?

But you should be aware that buying and selling the same securities within a single day—also known as day trading—can lead to your brokerage putting permanent limits on your account if you do it too many days in a row.

schwab.com - An Introduction to Pattern Day Trader Rules - Charles Schwab

At issue here are rules related to margin requirements for pattern day traders. Created by the Financial Industry Regulatory Authority (FINRA) after the tech bubble popped back in the early 2000's, the updated margin rules effectively hold pattern day traders using margin accounts to higher standards than people investing with cash accounts (or not exhibiting PDT behavior in margin accounts) by requiring them to keep larger amounts of cash and/or securities in their accounts. It works like this: If a trader makes four or more day trades, buying or selling (or selling and buying) the same security within a single day, over the course of any five business days in a margin account, and those trades account for more than 6% of their account activity over the period, the trader's account will be flagged as a pattern day trader account. If this happens, even inadvertently, the trader will have to maintain a minimum balance of $25,000 in the flagged account—on a permanent basis. If a pattern day trader account holds less than the $25,000 minimum at the close of a business day, the trader will be limited on the following day to making liquidating trades only. Not every trader wants to maintain $25,000 in their account, so it's important to pay close attention to your trades to make sure you don't end up with a flagged account. That said, Schwab does allow a one-time exception to clients who may have been flagged as day traders, so long as they commit not using the account for pattern day trading going forward.

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