Pandas contains extensive capabilities and features for working with time series data for all domains.

**Timestamp :** references particular moment in time and associates values with data points in time. Timestamped data is the most basic type of time series data that associates values with points in time. For pandas objects it means using the points in time.

ROC (Receiver Operator Characteristic) graphs are useful for consolidating the information from a ton of confusion matrices into a single, easy to interpret graph.

The purpose of this article is to serve as an introduction to ROC graphs and we’ll talk about:

*Classifier performance**ROC space**Random performance**Curves in ROC space**ROC curve in Python*

A classifier is a mapping from instances to predicted classes. Some classification models produce a **continuous** output (an estimate of the class membership probability) to which different thresholds may be applied to predict class membership. …

Determines cross-validated training and test scores for different training set sizes.

A cross-validation generator splits the whole dataset k times in training and test data. Subsets of the training set with varying sizes will be used to train the estimator and a score for each training subset size and the test set will be computed. Afterwards, the scores will be averaged over all k runs for each training subset size.

learning_curve(estimator,X,y,groups=None,train_sizes=np.linspace(0.1, 1.0, 5),cv=None,scoring=None,exploit_incremental_learning=False,n_jobs=None,pre_dispatch=”all”, verbose=0,shuffle=False,random_state=None,error_score=np.nan,return_times=False)

Let’s see how learning_curve() do the splits if **shuffle**=False. To do so, we…

Pick a performance metric that reflects the kind of task your algorithm is performing and how you would like your algorithm to behave.

In **classification** problems we are working with *discrete* data and trying to make discrete predictions, therefore, below are some metrics that are widely used in classification problems.

A two-by-two **confusion matrix** can be constructed representing the dispositions of the set of instances.

This article presents the basics of NumPy , a package for scientific computing with Python.

We’ll cover a few categories of basic array manipulations here:

*Creating NumPy arrays**Reshaping of arrays**Math operations with NumPy**Indexing and slicing of arrays**Iterating over arrays*

First, let’s import NumPy as np. This lets us use the shortcut np to refer to NumPy.