Is it possible to have 100% accuracy?

Yes, a predictive model with 100% accuracy is possible.

Is 100 percent accuracy possible?

The answer is “NO”. A high accuracy measured on the training set is the result of Overfitting. So, what does this overfitting means? Overfitting occurs when our machine learning model tries to cover all the data points or more than the required data points present in the given dataset.

Is 70% a good accuracy?

There is a general rule when it comes to understanding accuracy scores: Over 90% - Very good. Between 70% and 90% - Good. Between 60% and 70% - OK.

Is 100% accuracy bad in machine learning?

Achieving 100% machine learning model accuracy is typically a sign of some error, such as overfitting; that is, the model learns the characteristics of the training set so specifically that it cannot generalize to unseen data in the validation and evaluation sets.

Why do I get a 100% accuracy decision tree?

You are getting 100% accuracy because you are using a part of training data for testing. At the time of training, decision tree gained the knowledge about that data, and now if you give same data to predict it will give exactly same value. That's why decision tree producing correct results every time.

The perfect chess game (100% accuracy)

Is 99 accuracy overfitting?

If your classifier is "99% accurate", either you're using the wrong metric (a metric this high is not informative), or you have an overfitting or leakage problem.

What is the maximum number of decision trees?

The maximum accuracy is achieved at 200 trees. Further increasing the number of trees increases the training and execution time without significant accuracy benefits, however it slightly reduces the variance.

Is 90% a good accuracy in machine learning?

Good accuracy in machine learning is subjective. But in our opinion, anything greater than 70% is a great model performance. In fact, an accuracy measure of anything between 70%-90% is not only ideal, it's realistic. This is also consistent with industry standards.

Is 99 percent accuracy good in machine learning?

In most use cases, the human user will not be able to distinguish a model accuracy of 95% from 99%. Both models will be considered “good,” meaning that they solve the underlying problem that the model is supposed to solve.

What is the maximum accuracy of machine learning?

Accuracy comes out to 0.91, or 91% (91 correct predictions out of 100 total examples).

What does 2% accuracy mean?

Accuracy may be represented as a percentage as well as digits. Example: an accuracy of ±2%, +2 digits means 100.0 V reading on a multimeter can be from 97.8 V to 102.2 V. Accuracy is generally compared to an accepted industry standard.

Is 85% good accuracy?

In the ubiquitous computing community, there is an unofficial standard that 85% accuracy is "good enough" for sensing based on machine learning. But it's not so simple to say that 85% should be your target accuracy to consider a system useful.

Is 86 accuracy good in chess?

I'd say for me, anything over 80% leaves me feeling pretty good about my play. For a game that is at least 25 moves long where most of the moves were not forced, I would have to say that 90%+ accuracy (expert strength or better) is generally an indicator of a good game.

Is 99 accuracy cheating chess?

A player that has an accuracy of 99% is not necessarily cheating. But a player that doesn't make any mistakes throughout the game is at least suspicious.

What is 0.1% accuracy?

When manufacturers define their accuracy as “% of reading”, they are describing the accuracy as a percentage of the reading currently displayed. For example, a gauge with 0.1 % of reading accuracy that displays a reading of 100 psi would be accurate to ± 0.1 psi at that pressure.

What is top 1 percent accuracy?

Top-1 accuracy is the conventional accuracy, model prediction (the one with the highest probability) must be exactly the expected answer. It measures the proportion of examples for which the predictedlabel matches the single target label.

What is 90% accuracy in data entry?

Let's say, for example, that you need to write an email to the principal that contains 500 words. If you were typing with 90% accuracy, that means that 50 of these words would contain errors!

What does 99.9 accuracy mean?

Let's say you have to do a task 100 times without failure. If you do it correctly 99% of the time, there's a 63% chance you will fail. If you do it correctly 99.9% of the time, there's only a 10% chance you will fail.

How much accuracy is overfitting?

If our model does much better on the training set than on the test set, then we're likely overfitting. For example, it would be a big red flag if our model saw 99% accuracy on the training set but only 55% accuracy on the test set.

What does 100 accuracy mean machine learning?

That you have 100% train and test accuracy probably means that your model is massively overfitting because of your amount of data. But in general you should avoid overfitting as well as underfitting because both damage your performance of machine learning algorithms.

Is 80% accuracy good chess?

Players from 1000 to 1499 have normally 60 to 75 percent accuracy while players from 1500 to 1999 have 70 to 75 percent accuracy. PLayers with a rating of 2000 to 2499 have about 75 to 80 accuracy, while players from 2500 to 2999 have around 80 to 84 accuracy. PLayers beyond 3000 rating have around 85 percent accuracy.

How much accuracy is good for CNN?

Building CNN Model with 95% Accuracy | Convolutional Neural Networks.

How deep can a decision tree be?

It can also be described as the length of the longest path from the tree root to a leaf. The root node is considered to have a depth of 0. The Max Depth value cannot exceed 30 on a 32-bit machine. The default value is 30.

What is the perfect decision tree?

The decision tree is said to be complete when all the test conditions lead to a leaf node. The leaf node contains the class-labels, which vote in favor or against the decision.

How accurate are decision trees?

The decision tree classifier gave an accuracy of 91%. It means 6 observations have been classified as false. Let us first visualize the model prediction results.