Global accuracy

Model accuracy refers to the degree to which a machine learning model correctly predicts the target outcome based on the input data. It is a common classification performance metric used to evaluate the effectiveness of a machine learning model. Model accuracy is typically expressed as a percentage, with 100% accuracy meaning that the model has correctly predicted the target outcome for all examples in the evaluation dataset.

NextBrain will provide you with model accuracy for both training and test set. Keep in mind that model accuracy is only one aspect of model performance and may not always be the most important metric, especially in situations where the consequences of false predictions are severe, such as in medical diagnosis or fraud detection. In such cases, other metrics such as precision, recall, and F1 score may be more relevant. We can see the definition for such metrics as follows:

Precision: Proportion of positive predictions that are actually correct.
Recall (Sensitivity, Hit Rate): Proportion of actual positive cases that are correctly identified by the model.
F1 Score: Harmonic mean of precision and recall.

Meanwhile, you can evaluate the performance of a regression model in machine learning model with the following metrics:

Mean Absolute Error (MAE): The average absolute difference betw

Question 2

Feature importance

Accepted Answer

Feature importance

NextBrain demonstrates the Sankey diagram for each model. A Sankey diagram is a visual representation of the flow of predictions in a model, showing the relative importance of each feature in the dataset towards the final prediction. The width of each arrow corresponds to the amount of information or resources that is flowing through that particular channel. The colors used in a Sankey diagram indicate which flow separation is most likely. Darker colors are used to indicate higher probabilities, while lighter colors may indicate lower probabilities.

Sankey diagrams can provide context on the relationships between features and the model's predictions, whereas feature importance bars are limited to just showing the magnitude of the impact of each feature.

A Sample Sankey Diagram

It's important to note that feature importance alone does not guarantee that a feature is useful in the context of the problem and that other methods such as correlation and visualization should be used to complement feature importance.

Question 3

Advanced Statistics

Accepted Answer

Machine Learning Evaluation Metrics

There are many metrics used to evaluate machine learning models, each with their own pros and cons. We can broadly group metrics as classification and regression metrics.

Classification Metrics

Classification metrics are used to evaluate the performance of machine learning models that are used for predicting discrete values. Before proceeding to explain some frequently-used classification metrics, it is crucial to understand the confusion matrix shown below:

Confusion Matrix

Accuracy: This measures the proportion of correct predictions made by the model. It is easy to understand and interpret, but can be misleading in imbalanced datasets.

Precision: This measures the proportion of true positive predictions out of all positive predictions made by the model. It is useful for evaluating models that need to make few false positive predictions, such as in medical diagnosis.

Recall: This measures the proportion of true positive predictions out of all actual positive instances. It is useful for evaluating models that need to correctly identify as many positive instances as possible, such as in fraud detection.

![](https://storage.crisp.chat/use

Question 4

Visual Explainability

Accepted Answer

Visual Explainability

Machine learning methods use statistical learning to identify boundaries. One example of a machine learning method is a decision tree. A decision tree uses if-then statements to define patterns in data. In machine learning, these statements are called forks, and they split the data into two branches based on some value. That value between the branches is called a split point. A split point is the decision tree's version of a boundary. Every fork is adding information about data.

The distribution of the target variable based on some features refers to the way the values of the target variable (the variable being predicted) are spread out and grouped in relation to the values of one or more other features in the dataset. This information can give insights into the relationship between the target variable and the features, and help in building more accurate models.

For example, consider a dataset that contains information about marketing and sales. If the target variable is the ROAS, the distribution of the target variable based on the number of bedrooms could show how the average price of houses changes as the number of bedrooms increases or decreases. This information can be visualized in a plot, such as a bar graph or scatter plot, to make it easier to interpret.

Question 5

Column to predict

Accepted Answer

In machine learning, the column to predict, also known as the target variable or response variable, is the column whose value is being predicted by the model. The target variable is dependent on the input features and the goal of the model is to learn the relationship between the input features and the target variable in order to make accurate predictions.

For example, in a supervised learning problem, the input features may be age, income, education, and occupation, and the target variable may be the purchase decision of a customer. The goal of the model is to learn the relationship between the input features and the target variable and make predictions on new, unseen data.

In some cases, the target variable can be binary (e.g. yes/no) or continuous (e.g. salary). The type of target variable will influence the type of model used for prediction and the evaluation metrics used to measure the performance of the model.

Question 6

Column effect importance

Accepted Answer

Column effect importance in machine learning refers to the measurement of the effect of each feature (column) in a dataset on the outcome being predicted. The purpose is to understand how each feature influences the prediction and to identify which features have the greatest impact on the model's accuracy.

For example, consider a dataset that contains information about marketing and sales. The columns might include the number of clicks, the gender of customers, the location, and the facebook campaign id. Column effect importance would help determine which of these factors has the most significant effect on the ROAS, and how each feature interacts with the other features in the dataset.

A Sample Column Importance Graph

NextBrain performs SHAP analysis to quantify the contribution of each feature to the prediction of an instance. These values can be positive or negative, indicating the increase or decrease in the predicted outcome that is due to a particular feature. Overall, SHAP values provide a flexible and powerful tool for understanding and interpreting the predictions of machine learning models, and can be useful in a wide range of applications. You will get recommendations about which columns contribute poorly to the prediction on the Model Summary panel.

![A Sample Shap Analysis](https://storage.crisp.chat/users/helpdesk/website/ac02aa4ca9b97000/13shap_n8qa52.

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