Dictionary for Artificial intelligence
Online free GPU : https://hackernoon.com/train-your-machine-learning-models-on-googles-gpus-for-free-forever-a41bd309d6ad
- Bias and Variance: Bias is how far off on the average the model is from the truth and The variance is how much that the estimate varies around its average.
The error due to bias is taken as the difference between the expected (or average) prediction of our model and the correct value which we are trying to predict. Of course you only have one model so talking about expected or average prediction values might seem a little strange. However, imagine you could repeat the whole model building process more than once: each time you gather new data and run a new analysis creating a new model. Due to randomness in the underlying data sets, the resulting models will have a range of predictions. Bias measures how far off in general these models’ predictions are from the correct value.
The error due to variance is taken as the variability of a model prediction for a given data point. Again, imagine you can repeat the entire model building process multiple times. The variance is how much the predictions for a given point vary between different realizations of the model.
As more and more parameters are added to a model, the complexity of the model rises and variance becomes our primary concern while bias steadily falls. For example, as more polynomial terms are added to a linear regression, the greater the resulting model’s complexity will be. In other words, bias has a negative first-order derivative in response to model complexity while variance has a positive slope.
High bias can cause an algorithm to miss the relevant relations between features and target outputs (under fitting). Low complexity will result in poor accuracy (and thus high error) for both training and test data.
High variance can cause an algorithm to model the random noise in the training data, rather than the intended outputs (over fitting). high complexity models will result in a low training error and a high test error.
The best complexity lies where the test error reaches a minimum, that is, somewhere in between a very simple and a very complex model. Over fitting results in low training error and high test error, while under fitting results in high errors in both the training and test set. Theoretically, with a complex enough model, as the number of samples tends toward infinity the variance tends toward zero. A better approach to reducing variance is to use regularization. That is, in addition to rewarding your model as it models the training data well, penalize it for growing too complex. Essentially, regularization injects “bias” into the model by telling it not to become too complex. Common regularization techniques include lasso or ridge regression, dropout for neural networks, and soft margin SVMs. The image below explain the bias-variance trade-off
2) Explain how a ROC curve works
An ROC curve (Receiver Operating Characteristic curve) is a commonly used way to visualize the performance of a binary classifier, meaning a classifier with two possible output classes. It is a plot of the True Positive Rate (on the y-axis) versus the False Positive Rate (on the x-axis) for every possible classification threshold. In a ROC curve the true positive rate (Sensitivity) is plotted in function of the false positive rate (100-Specificity) for different cut-off points of a parameter. Each point on the ROC curve represents a sensitivity/specificity pair corresponding to a particular decision threshold. The area under the ROC curve (AUC) is a measure of how well a parameter can distinguish between two groups .
Accuracy is measured by the area under the ROC curve. An area of 1 represents a perfect test; an area of .5 represents a worthless test.
link-http://www.dataschool.io/roc-curves-and-auc-explained/
3)Define precision and recall
precision (also called positive predictive value) is the fraction of relevant instances among the retrieved instances, while recall (also known as sensitivity) is the fraction of relevant instances that have been retrieved over the total amount of relevant instances. Precision is the number of document retrieved that are relevant and Recall is the number of relevant document that are retrieved. Precision and Recall are inversely proportional to each other
Suppose a computer program for recognizing dogs in photographs identifies eight dogs in a picture containing 12 dogs and some cats. Of the eight dogs identified, five actually are dogs (true positives), while the rest are cats (false positives). The program’s precision is 5/8 while its recall is 5/12.
