The four commonly used metrics for evaluating classifier performance are:

1. Accuracy: The proportion of correct predictions out of the total predictions.

2. Precision: The proportion of true positive predictions out of the total positive predictions (precision = true positives / (true positives + false positives)).

3. Recall (Sensitivity or True Positive Rate): The proportion of true positive predictions out of the total actual positive instances (recall = true positives / (true positives + false negatives)).

4. F1 Score: The harmonic mean of precision and recall, providing a balance between the two metrics (F1 score = 2 * ((precision * recall) / (precision + recall))).

Root Mean Squared Error (RMSE)and Mean Absolute Error (MAE) are metrics used to evaluate a Regression Model. These metrics tell us how accurate our predictions are and, what is the amount of deviation from the actual values.

What is linear regression?

Linear regression analysis is used to predict the value of a variable based on the value of another variable. The variable you want to predict is called the dependent variable. The variable you are using to predict the other variable's value is called the independent variable.

Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable. For example, a modeler might want to relate the weights of individuals to their heights using a linear regression model.

A linear regression line has an equation of the form Y = a + bX, where X is the explanatory variable and Y is the dependent variable. The slope of the line is b, and a is the intercept (the value of y when x = 0).

For linear regression Y=a+bx+error.

If neglect error then Y=a+bx. If x increases by 1, then Y = a+b(x+1) which implies Y=a+bx+b. So Y increases by its slope.

For linear regression Y=a+bx+error. If neglect error then Y=a+bx. If x increases by 1, then Y = a+b(x+1) which implies Y=a+bx+b. So Y increases by its slope.

Binary vs. Multi-Class Classification

Classification problems are common in machine learning. In most cases, developers prefer using a supervised machine-learning approach to predict class tables for a given dataset. Unlike regression, classification involves designing the classifier model and training it to input and categorize the test dataset. For that, you can divide the dataset into either binary or multi-class modules.

As the name suggests, binary classification involves solving a problem with only two class labels. This makes it easy to filter the data, apply classification algorithms, and train the model to predict outcomes. On the other hand, multi-class classification is applicable when there are more than two class labels in the input train data. The technique enables developers to categorize the test data into multiple binary class labels.

That said, while binary classification requires only one classifier model, the one used in the multi-class approach depends on the classification technique. Below are the two models of the multi-class classification algorithm.

One-Vs-Rest Classification Model for Multi-Class Classification

Also known as one-vs-all, the one-vs-rest model is a defined heuristic method that leverages a binary classification algorithm for multi-class classifications. The technique involves splitting a multi-class dataset into multiple sets of binary problems. Following this, a binary classifier is trained to handle each binary classification model with the most confident one making predictions.

For instance, with a multi-class classification problem with red, green, and blue datasets, binary classification can be categorized as follows:

Problem one: red vs. green/blue

Problem two: blue vs. green/red

Problem three: green vs. blue/red

The only challenge of using this model is that you should create a model for every class. The three classes require three models from the above datasets, which can be challenging for large sets of data with million rows, slow models, such as neural networks and datasets with a significant number of classes.

The one-vs-rest approach requires individual models to prognosticate the probability-like score. The class index with the largest score is then used to predict a class. As such, it is commonly used for classification algorithms that can naturally predict scores or numerical class membership such as perceptron and logistic regression.

The area under the ROC curve (AUC) is a common evaluation metric for binary classification, which measures the performance of a classifier at different threshold values for the predicted probabilities. Other common metrics include accuracy, precision, recall, and F1 score, which are based on the confusion matrix of true positives, false positives, true negatives, and false negatives.

What is a Share?

Shares are named Snowflake objects that encapsulate all of the information required to share a database.

Data providers add Snowflake objects (databases, schemas, tables, secure views, etc.) to a share using either or both of the following options:

Option 1: Grant privileges on objects to a share via a database role.

Option 2: Grant privileges on objects directly to a share.

You choose which accounts can consume data from the share by adding the accounts to the share.

After a database is created (in a consumer account) from a share, all the shared objects are accessible to users in the consumer account.

Shares are secure, configurable, and controlled completely by the provider account:

* New objects added to a share become immediately available to all consumers, providing real-time access to shared data.

Access to a share (or any of the objects in a share) can be revoked at any time.