10 Machine Learning Evaluation Techniques You Need to Know About In 2021

Machine Learning Model Evaluation Techniques

Artificial intelligence and machine learning are the two most popular tech concepts prevailing in the world today. Every single company out there wants to roll out a product or a solution that’s powered by machine learning technology. Thanks to this market, the global market value of this industry is expected to reach around $117.19bn by 2027.

Apart from offering lucrative opportunities to business owners, it is also continuously elevating our lifestyle. The purpose of any machine learning model is to give accurate predictions and outcomes to ultimately deliver impactful values in people’s lives. However, for machine learning algorithms and models to perform well, there has to be evaluation practices and techniques in place. This article is all about that.

Machine Learning Model evaluation has a dominant role in developing a predictive machine learning model. Building a fit model for predictions without checking their precision and accuracy is not countable but the max precisions and accuracy it does.

To enhance the accuracy, we need to check on the metrics and make improvements accordingly until you get your desired accuracy rate.

This article, covers top 10 most important technique which are as follows:

  1. Chi-Square
  2. Confusion Matrix
  3. Concordant-Discordant Ratio
  4. Confidence Interval
  5. Gini Coefficient
  6. Gain and Lift Chart
  7. Kolmogorov-Smirnov Chart
  8. Predictive Power
  9. AUC-ROC Curve
  10. Root Mean Square Error

1. Chi-Square

This is one of the common evaluation models, where it is used to check the independence between two variables of a hypothesis between two or more groups.

It is used to analyse Categorical Data and evaluate the test of bivariate tables.

Chi-Square is been calculated by formula:


O = Observed Frequency

E = Expected Frequency

2. Confusion Matrix

The Confusion Matrix is also well known as Error Matrix which is basically used as a classification model on a set of test dataset. As mentioned in figure below, (0) is represented as a negative table and (1) is represented as a positive table where each row says about instances in the predictive class and each column says about instances of the actual class.

True Positive (TP): Correct predicts the positive class.

True Negative (TN): Correct predicts the negative class.

False Positive (FP): Incorrect predicts a negative class.

False Negative (FN): Incorrect predicts a positive class.

Confusion Matrix

3. Concordant-Discordant Ratio

In a pair of cases, a concordant pair is the one that has one case higher on both variables than other cases. Conversely, a discordant pair is one in which a pair of cases is higher on one variable than the other case but lower on the other variable.

Consider two pairs (Xa, Ya) and (Xb, Yb)

Concordant = Xa>Xb and Ya>Yb or Xa<Xb and Ya<Yb

Discordant = Xa>Xb and Ya<Yb or Xa<Xb and Ya>Yb.

4. Confidence Interval

A specified probability that the value of a parameter lies within it is known as Confidence Interval. To know the possible range around the targeted estimate value, confidence interval is used.

In the domain of supervised machine learning, Confidence Intervals basically consist of a range of potential values of an unknown population parameter and the factors which are affecting the width of the confidence interval are the confidence level, size as well as variability of the sample.

5. Gini Coefficient

A matrix which calculates balance and imbalance class values is well known as the Gini coefficient.

The range of the Gini coefficient matrix is 0 and 1.

  • 0 = Perfect Balance
  • 1 = Perfect Imbalance

6. Gain and Lift Chart

Gain and Lift Chart is calculated as a ratio of the results gained with and without a model.

This model evaluation is highly used in classification problems. Where,

Gain and Lift Chart

7. Kolmogorov-Smirnov Chart

The degree of separation between the positive and negative distribution which is carried out by a non-parametric statistical classification model.

Kolmogorov-Smirnov is used for the comparison of one sample with another.

8. Predictive Power

A synthetic metric which satisfies interesting properties. Value of predictive power metrics is between 0 and 1. Where,

  • 0 = No predictive power.
  • 1 = Maximum predictive power.

9. AUC-ROC Curve

ROC or Receiver Operating Characteristics Curve is one of the highly recommended for classification problem models. Plotting is based on two main parameters:

  • False Positive Rates (FPR)
  • True Positive Rates (TPR)

The quality of the classification model is measured as Area under ROC curve. Mentioned parameters can be formulated as:

AUC-ROC Curve Chart

10. Root Mean Square Error

Difference between values predicted by model and value actually observed is called the Root Mean Square Error (RMSE). RMSE can be formulated as:

Root Mean Square Error

Wrapping Up

These evaluation models will help you optimize your machine learning model for performance. Based on your requirements, you can use one or all and then pave the way for a smooth-functioning machine learning model. For your product or service to be successful in the market, care has to be taken to make it as airtight as possible. Even one single loophole could provide an opportunity for others to leverage and surge ahead in your domain.

So, stay alert and keep optimizing your model to perfection.

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Vatsal Patel is a trained computer engineer and avid BI developer. With over four years of developer experience, Vatsal tends to learn about artificial intelligence every day. He likes to know about the development of AI today and further study the potential of AI in the future in his free time. Still, he is also a Microsoft certified Azure AI engineer.

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