12 Important Model Evaluation Metrics for Machine Learning Everyone Should Know (Updated 2024)

Tavish Srivastava Last Updated : 21 Oct, 2024
19 min read

The idea of building machine learning models or artificial intelligence or deep learning models works on a constructive feedback principle. You build a model, get feedback from metrics, make improvements, and continue until you achieve a desirable classification accuracy. Evaluation metrics explain the performance of the model. An important aspect of evaluation metrics is their capability to discriminate among model results.

This article explains 12 important evaluation metrics you must know to use as a data science professional. You will learn their uses, advantages, and disadvantages, which will help you choose and implement each of them accordingly.

Also, in this article, we explore understanding ML metrics, evaluation metrics in machine learning, and performance metrics in machine learning, highlighting the importance of out-of-sample evaluation metrics in data science for robust model assessment.

Learning Objectives

  • In this tutorial, you will learn about several evaluation metrics in machine learning, like confusion matrix, cross-validation, AUC-ROC curve, and many more classification metrics.
  • You will also learn about the different metrics used for logistic regression for different problems.
  • Lastly, you will learn about cross-validation.

If you’re starting out your machine learning journey, you should check out the comprehensive and popular Applied Machine Learning course that covers this concept in a lot of detail along with the various algorithms and components of machine learning.

What Are Evaluation Metrics?

Evaluation metrics are quantitative measures used to assess the performance and effectiveness of a statistical or machine learning model. These metrics provide insights into how well the model is performing and help in comparing different models or algorithms.

When evaluating a machine learning model, it is crucial to assess its predictive ability, generalization capability, and overall quality. Evaluation metrics provide objective criteria to measure these aspects. The choice of evaluation metrics depends on the specific problem domain, the type of data, and the desired outcome.

I have seen plenty of analysts and aspiring data scientists not even bothering to check how robust their model is. Once they are finished building a model, they hurriedly map predicted values on unseen data. This is an incorrect approach. The ground truth is building a predictive model is not your motive. It’s about creating and selecting a model which gives a high accuracy_score on out-of-sample data. Hence, it is crucial to check the accuracy of your model prior to computing predicted values.

In our industry, we consider different kinds of metrics to evaluate our ml models. The choice of evaluation metric completely depends on the type of model and the implementation plan of the model. After you are finished building your model, these 12 metrics will help you in evaluating your model’s accuracy. Considering the rising popularity and importance of cross-validation, I’ve also mentioned its principles in this article.

Types of Predictive Models

When we talk about predictive models, we are talking either about a regression model (continuous output) or a classification model (nominal or binary output). The evaluation metrics used in each of these models are different.

In classification problems, we use two types of algorithms (dependent on the kind of output it creates):

  1. Class output: Algorithms like SVM and KNN create a class output. For instance, in a binary classification problem, the outputs will be either 0 or 1. However, today we have algorithms that can convert these class outputs to probability. But these algorithms are not well accepted by the statistics community.
  2. Probability output: Algorithms like Logistic Regression, Random Forest, Gradient Boosting, Adaboost, etc., give probability outputs. Converting probability outputs to class output is just a matter of creating a threshold probability.

In regression problems, we do not have such inconsistencies in output. The output is always continuous in nature and requires no further treatment.

Illustrative Example

For a classification model evaluation metrics discussion, I have used my predictions for the problem BCI challenge on Kaggle. The solution to the problem is out of the scope of our discussion here. However, this article uses the final predictions on the training set. The model produced probability outputs for this problem, which have been converted to class outputs assuming a threshold of 0.5.

Here are 12 important model evaluation metrics commonly used in machine learning:

Confusion Matrix

A confusion matrix is an N X N matrix, where N is the number of predicted classes. For the problem in hand, we have N=2, and hence we get a 2 X 2 matrix. It is a performance measurement for machine learning classification problems where the output can be two or more classes. Confusion matrix is a table with 4 different combinations of predicted and actual values. It is extremely useful for measuring precision-recall, Specificity, Accuracy, and most importantly, AUC-ROC curves.

Here are a few definitions you need to remember for a confusion matrix:

  • True Positive: You predicted positive, and it’s true.
  • True Negative: You predicted negative, and it’s true.
  • False Positive: (Type 1 Error): You predicted positive, and it’s false.
  • False Negative: (Type 2 Error): You predicted negative, and it’s false.
  • Accuracy: the proportion of the total number of correct predictions that were correct.
  • Positive Predictive Value or Precision: the proportion of positive cases that were correctly identified.
  • Negative Predictive Value: the proportion of negative cases that were correctly identified.
  • Sensitivity or Recall: the proportion of actual positive cases which are correctly identified.
  • Specificity: the proportion of actual negative cases which are correctly identified.
  • Rate: It is a measuring factor in a confusion matrix. It has also 4 types TPR, FPR, TNR, and FNR.
zconfusion matrix, evaluation metrics
confusion matrix | evaluation metrics

The accuracy for the problem in hand comes out to be 88%. As you can see from the above two tables, the Positive Predictive Value is high, but the negative predictive value is quite low. The same holds for Sensitivity and Specificity. This is primarily driven by the threshold value we have chosen. If we decrease our threshold value, the two pairs of starkly different numbers will come closer.

In general, we are concerned with one of the above-defined metrics. For instance, in a pharmaceutical company, they will be more concerned with a minimal wrong positive diagnosis. Hence, they will be more concerned about high Specificity. On the other hand, an attrition model will be more concerned with Sensitivity. Confusion matrices are generally used only with class output models.

F1 Score

In the last section, we discussed precision and recall for classification problems and also highlighted the importance of choosing a precision/recall basis for our use case. What if, for a use case, we are trying to get the best precision and recall at the same time? F1-Score is the harmonic mean of precision and recall values for a classification problem. The formula for F1-Score is as follows:

F1 score | evaluation metrics

Now, an obvious question that comes to mind is why you are taking a harmonic mean and not an arithmetic mean. This is because HM punishes extreme values more. Let us understand this with an example. We have a binary classification model with the following results:

Precision: 0, Recall: 1

Here, if we take the arithmetic mean, we get 0.5. It is clear that the above result comes from a dumb classifier that ignores the input and predicts one of the classes as output. Now, if we were to take HM, we would get 0 which is accurate as this model is useless for all purposes.

This seems simple. There are situations, however, for which a data scientist would like to give a percentage more importance/weight to either precision or recall. Altering the above expression a bit such that we can include an adjustable parameter beta for this purpose, we get:

F-beta formula | evaluation metrics

Fbeta measures the effectiveness of a model with respect to a user who attaches β times as much importance to recall as precision.

Gain and Lift Charts

Gain and Lift charts are mainly concerned with checking the rank ordering of the probabilities. Here are the steps to build a Lift/Gain chart:

  • Step 1: Calculate the probability for each observation
  • Step 2: Rank these probabilities in decreasing order.
  • Step 3: Build deciles with each group having almost 10% of the observations.
  • Step 4: Calculate the response rate at each decile for Good (Responders), Bad (Non-responders), and total.

You will get the following table from which you need to plot Gain/Lift charts:

gain and lift charts data table

This is a very informative table. The cumulative Gain chart is the graph between Cumulative %Right and Cumulative %Population. For the case in hand, here is the graph:

cumulative gain chart, evaluation metrics

This graph tells you how well is your model segregating responders from non-responders. For example, the first decile, however, has 10% of the population, has 14% of the responders. This means we have a 140% lift at the first decile.

What is the maximum lift we could have reached in the first decile? From the first table of this article, we know that the total number of responders is 3850. Also, the first decile will contain 543 observations. Hence, the maximum lift at the first decile could have been 543/3850 ~ 14.1%. Hence, we are quite close to perfection with this model.

Let’s now plot the lift curve. The lift curve is the plot between total lift and %population. Note that for a random model, this always stays flat at 100%. Here is the plot for the case in hand:

lift chart, evaluation metrics

You can also plot decile-wise lift with decile number:

lift decile chart, evaluation metrics

What does this graph tell you? It tells you that our model does well till the 7th decile. Post which every decile will be skewed towards non-responders. Any model with lift @ decile above 100% till minimum 3rd decile and maximum 7th decile is a good model. Else you might consider oversampling first.

Lift / Gain charts are widely used in campaign targeting problems. This tells us to which decile we can target customers for a specific campaign. Also, it tells you how much response you expect from the new target base.

Kolomogorov Smirnov Chart

K-S or Kolmogorov-Smirnov chart measures the performance of classification models. More accurately, K-S is a measure of the degree of separation between the positive and negative distributions. The K-S is 100 if the scores partition the population into two separate groups in which one group contains all the positives and the other all the negatives.

On the other hand, If the model cannot differentiate between positives and negatives, then it is as if the model selects cases randomly from the population. The K-S would be 0. In most classification models, the K-S will fall between 0 and 100, and the higher the value, the better the model is at separating the positive from negative cases.

For the case in hand, the following is the table:

Kolomogorov Smirnov, ks data table

We can also plot the %Cumulative Good and Bad to see the maximum separation. Following is a sample plot:

model evaluation, Kolomogorov Smirnov, KS | evaluation metrics

The evaluation metrics covered here are mostly used in classification problems. So far, we’ve learned about the confusion matrix, lift and gain chart, and kolmogorov-smirnov chart. Let’s proceed and learn a few more important metrics.

Area Under the ROC Curve (AUC – ROC)

This is again one of the popular evaluation metrics used in the industry. The biggest advantage of using the ROC curve is that it is independent of the change in the proportion of responders. This statement will get clearer in the following sections.

Let’s first try to understand what the ROC (Receiver operating characteristic) curve is. If we look at the confusion matrix below, we observe that for a probabilistic model, we get different values for each metric.

confusion matrix for ROC, model evaluation metrics

Hence, for each sensitivity, we get a different specificity. The two vary as follows:

specificity, sensitivity | evaluation metrics

The ROC curve is the plot between sensitivity and (1- specificity). (1- specificity) is also known as the false positive rate, and sensitivity is also known as the True Positive rate. Following is the ROC curve for the case in hand.

model evaluation, ROC curve | evaluation metrics

Let’s take an example of threshold = 0.5 (refer to confusion matrix). Here is the confusion matrix:

confusion matrix

As you can see, the sensitivity at this threshold is 99.6%, and the (1-specificity) is ~60%. This coordinate becomes on point in our ROC curve. To bring this curve down to a single number, we find the area under this curve (AUC).

Note that the area of the entire square is 1*1 = 1. Hence AUC itself is the ratio under the curve and the total area. For the case in hand, we get AUC ROC as 96.4%. Following are a few thumb rules:

  • .90-1 = excellent (A)
  • .80-.90 = good (B)
  • .70-.80 = fair (C)
  • .60-.70 = poor (D)
  • .50-.60 = fail (F)

We see that we fall under the excellent band for the current model. But this might simply be over-fitting. In such cases, it becomes very important to do in-time and out-of-time validations.

Points to Remember

1. A model that gives class as output will be represented as a single point in the ROC plot.

2. You cannot compare such models directly because the judgment relies on a single metric rather than multiple metrics. For instance, a model with parameters (0.2,0.8) and a model with parameters (0.8,0.2) can be coming out of the same model; hence these metrics should not be directly compared.

3. In the case of the probabilistic model, we were fortunate enough to get a single number which was AUC-ROC. But still, we need to look at the entire curve to make conclusive decisions. It is also possible that one model performs better in some regions and other performs better in others.

Advantages of Using ROC

Why should you use ROC and not metrics like the lift curve?

Lift is dependent on the total response rate of the population. Hence, if the response rate of the population changes, the same model will give a different lift chart. A solution to this concern can be a true lift chart (finding the ratio of lift and perfect model lift at each decile). But such a ratio rarely makes sense for the business.

The ROC curve, on the other hand, is almost independent of the response rate. This is because it has the two axes coming out from columnar calculations of the confusion matrix. The numerator and denominator of both the x and y axis will change on a similar scale in case of a response rate shift.

Log Loss

AUC ROC considers the predicted probabilities for determining our model’s performance. However, there is an issue with AUC ROC, it only takes into account the order of probabilities, and hence it does not take into account the model’s capability to predict a higher probability for samples more likely to be positive. In that case, we could use the log loss, which is nothing but a negative average of the log of corrected predicted probabilities for each instance.

Log loss formula, evaluation metrics
  • p(yi) is the predicted probability of a positive class
  • 1-p(yi) is the predicted probability of a negative class
  • yi = 1 for the positive class and 0 for the negative class (actual values)

Let us calculate log loss for a few random values to get the gist of the above mathematical function:

  • Log loss(1, 0.1) = 2.303
  • Log loss(1, 0.5) = 0.693
  • Log loss(1, 0.9) = 0.105

If we plot this relationship, we will get a curve as follows:

Log loss curve | evaluation metrics, model evaluation metrics

It’s apparent from the gentle downward slope towards the right that the Log Loss gradually declines as the predicted probability improves. Moving in the opposite direction, though, the Log Loss ramps up very rapidly as the predicted probability approaches 0.

So, the lower the log loss, the better the model. However, there is no absolute measure of a good log loss, and it is use-case/application dependent.

Whereas the AUC is computed with regards to binary classification with a varying decision threshold, log loss actually takes the “certainty” of classification into account.

Gini Coefficient

The Gini coefficient is sometimes used in classification problems. The Gini coefficient can be derived straight away from the AUC ROC number. Gini is nothing but the ratio between the area between the ROC curve and the diagonal line & the area of the above triangle. Following are the formulae used:

Gini = 2*AUC – 1

Gini above 60% is a good model. For the case in hand, we get Gini as 92.7%.

Concordant – Discordant Ratio

This is, again, one of the most important evaluation metrics for any classification prediction problem. To understand this, let’s assume we have 3 students who have some likelihood of passing this year. Following are our predictions:

A – 0.9
B – 0.5
C – 0.3

Now picture this. if we were to fetch pairs of two from these three students, how many pairs would we have? We will have 3 pairs: AB, BC, and CA. Now, after the year ends, we see that A and C passed this year while B failed. No, we choose all the pairs where we will find one responder and another non-responder. How many such pairs do we have?

We have two pairs AB and BC. Now for each of the 2 pairs, the concordant pair is where the probability of the responder was higher than the non-responder. Whereas discordant pair is where the vice-versa holds true. In case both the probabilities were equal, we say it’s a tie. Let’s see what happens in our case :

AB – Concordant
BC – Discordant

Hence, we have 50% of concordant cases in this example. A concordant ratio of more than 60% is considered to be a good model. This metric generally is not used when deciding how many customers to target etc. It is primarily used to access the model’s predictive power. Decisions like how many to target are again taken by KS / Lift charts.

Root Mean Squared Error (RMSE)

RMSE is the most popular evaluation metric used in regression problems. It follows an assumption that errors are unbiased and follow a normal distribution. Here are the key points to consider on RMSE:

  1. The power of ‘square root’ empowers this metric to show large number deviations.
  2. The ‘squared’ nature of this metric helps to deliver more robust results, which prevent canceling the positive and negative error values. In other words, this metric aptly displays the plausible magnitude of the error term.
  3. It avoids the use of absolute error values, which is highly undesirable in mathematical calculations.
  4. When we have more samples, reconstructing the error distribution using RMSE is considered to be more reliable.
  5. RMSE is highly affected by outlier values. Hence, make sure you’ve removed outliers from your data set prior to using this metric.
  6. As compared to mean absolute error, RMSE gives higher weightage and punishes large errors.

RMSE metric is given by:

model evaluation, rmse, root mean squared error | evaluation metrics

where N is the Total Number of Observations.

Root Mean Squared Logarithmic Error

In the case of Root mean squared logarithmic error, we take the log of the predictions and actual values. So basically, what changes are the variance that we are measuring? RMSLE is usually used when we don’t want to penalize huge differences in the predicted and the actual values when both predicted, and true values are huge numbers.

RMSE, RMSLE, evaluation metrics
  1. If both predicted and actual values are small: RMSE and RMSLE are the same.
  2. If either predicted or the actual value is big: RMSE > RMSLE
  3. If both predicted and actual values are big: RMSE > RMSLE (RMSLE becomes almost negligible)

R-Squared/Adjusted R-Squared

We learned that when the RMSE decreases, the model’s performance will improve. But these values alone are not intuitive.

In the case of a classification problem, if the model has an accuracy of 0.8, we could gauge how good our model is against a random model, which has an accuracy of 0.5. So the random model can be treated as a benchmark. But when we talk about the RMSE metrics, we do not have a benchmark to compare.

This is where we can use the R-Squared metric. The formula for R-Squared is as follows:

R squared formula | evaluation metrics
MSE formula | evaluation metrics

MSE(model): Mean Squared Error of the predictions against the actual values

MSE(baseline): Mean Squared Error of mean prediction against the actual values

In other words, how good is our regression model as compared to a very simple model that just predicts the mean value of the target from the train set as predictions?

Adjusted R-Squared

A model performing equal to the baseline would give R-Squared as 0. Better the model, the higher the r2 value. The best model with all correct predictions would give R-Squared of 1. However, on adding new features to the model, the R-Squared value either increases or remains the same. R-Squared does not penalize for adding features that add no value to the model. So an improved version of the R-Squared is the adjusted R-Squared. The formula for adjusted R-Squared is given by:

R squared | evaluation metrics

k: number of features

n: number of samples

As you can see, this metric takes the number of features into account. When we add more features, the term in the denominator n-(k +1) decreases, so the whole expression increases.

If R-Squared does not increase, that means the feature added isn’t valuable for our model. So overall, we subtract a greater value from 1 and adjusted r2, in turn, would decrease.

Beyond these 12 evaluation metrics, there is another method to check the model performance. These 7 methods are statistically prominent in data science. But, with the arrival of machine learning, we are now blessed with more robust methods of model selection. Yes! I’m talking about Cross Validation.

Though cross-validation isn’t really an evaluation metric that is used openly to communicate model accuracy, the result of cross-validation provides a good enough intuitive result to generalize the performance of a model.

Let’s now understand cross-validation in detail.

Cross Validation

Let’s first understand the importance of cross-validation. Due to my busy schedule these days, I don’t get much time to participate in data science competitions. A long time back, I participated in TFI Competition on Kaggle. Without delving into my competition performance, I would like to show you the dissimilarity between my public and private leaderboard scores.

Here Is an Example of Scoring on Kaggle!

For the TFI competition, the following were three of my solution and scores (the lesser, the better):

kaggle, model performance metrics, model evaluation metrics

You will notice that the third entry which has the worst Public score turned out to be the best model on Private ranking. There were more than 20 models above the “submission_all.csv”, but I still chose “submission_all.csv” as my final entry (which really worked out well). What caused this phenomenon? The dissimilarity in my public and private leaderboards is caused by over-fitting.

Over-fitting is nothing, but when your model becomes highly complex that it starts capturing noise, also. This ‘noise’ adds no value to the model but only inaccuracy.

In the following section, I will discuss how you can know if a solution is an over-fit or not before we actually know the test set results.

The Concept of Cross-Validation

Cross Validation is one of the most important concepts in any type of data modeling. It simply says, try to leave a sample on which you do not train the model and test the model on this sample before finalizing the model.

model evaulation, cross validation, model performance metrics

The above diagram shows how to validate the model with the in-time sample. We simply divide the population into 2 samples and build a model on one sample. The rest of the population is used for in-time validation.

Could there be a negative side to the above approach?

I believe a negative side of this approach is that we lose a good amount of data from training the model. Hence, the model is very high bias. And this won’t give the best estimate for the coefficients. So what’s the next best option?

What if we make a 50:50 split of the training population and the train on the first 50 and validate on the rest 50? Then, we train on the other 50 and test on the first 50. This way, we train the model on the entire population, however, on 50% in one go. This reduces bias because of sample selection to some extent but gives a smaller sample to train the model on. This approach is known as 2-fold cross-validation.

K-Fold Cross-Validation

Let’s extrapolate the last example to k-fold from 2-fold cross-validation. Now, we will try to visualize how a k-fold validation work.

kfolds, cross validation, evaluation metrics

This is a 7-fold cross-validation.

Here’s what goes on behind the scene: we divide the entire population into 7 equal samples. Now we train models on 6 samples (Green boxes) and validate on 1 sample (grey box). Then, at the second iteration, we train the model with a different sample held as validation. In 7 iterations, we have basically built a model on each sample and held each of them as validation. This is a way to reduce the selection bias and reduce the variance in prediction power. Once we have all 7 models, we take an average of the error terms to find which of the models is best.

How does this help to find the best (non-over-fit) model?

k-fold cross-validation is widely used to check whether a model is an overfit or not. If the performance metrics at each of the k times modeling are close to each other and the mean of the metric is highest. In a Kaggle competition, you might rely more on the cross-validation score than the Kaggle public score. This way, you will be sure that the Public score is not just by chance.

How do we implement k-fold with any model?

Coding k-fold in R and Python are very similar. Here is how you code a k-fold in Python:

Try out the code for KFold in the live coding window below:

# importing the required libraries
import numpy as np
from sklearn.model_selection import KFold

# create a sample dataset
X = np.array([[1, 2, 3, 2],
             [3, 4, 1, 1],
             [1, 2, 1, 1],
             [3, 4, 1, 1]])

y = np.array([1, 2, 3, 4])

# create the object of the KFold
kf = KFold(n_splits=2)

print(kf)
KFold(n_splits=2, random_state=None, shuffle=False)
for train_index, test_index in kf.split(X):
  print("TRAIN:", train_index, "TEST:", test_index)    
  X_train, X_test = X[train_index], X[test_index]
  y_train, y_test = y[train_index], y[test_index]


kf = KFold(n_splits=3)
print(kf)
KFold(n_splits=3, random_state=None, shuffle=False)
for train_index, test_index in kf.split(X):
  print("TRAIN:", train_index, "TEST:", test_index)    
  X_train, X_test = X[train_index], X[test_index]
  y_train, y_test = y[train_index], y[test_index]

But how do we choose k?

This is the tricky part. We have a trade-off to choosing k.
For a small k, we have a higher selection bias but a low variance in the performances.
For a large k, we have a small selection bias but a high variance in the performances.

Think of extreme cases:

k = 2: We have only 2 samples similar to our 50-50 example. Here we build the model only on 50% of the population each time. But as the validation is a significant population, the variance of validation performance is minimal.
k = a number of observations (n): This is also known as “Leave one out.” We have n samples and modeling repeated n number of times, leaving only one observation out for cross-validation. Hence, the selection bias is minimal, but the variance of validation performance is very large.

Generally, a value of k = 10 is recommended for most purposes.

Conclusion

Measuring the performance of the training sample is pointless. And leaving an in-time validation batch aside is a waste of data. K-Fold gives us a way to use every single data point, which can reduce this selection bias to a good extent.

In addition, the metrics covered in this article are some of the most used metrics of evaluation in classification and regression problems.

Which metric do you often use in classification and regression problems? Have you used k-fold cross-validation before for any kind of analysis? Did you see any significant benefits against using batch validation? Do let us know your thoughts about this guide in the comments section below.

Hope you find this information on ML metrics and evaluation metrics in machine learning helpful for your projects! Understanding performance metrics and out-of-sample evaluation metrics in data science is essential for success.

Key Takeaways

  • Evaluation metrics measure the quality of the machine learning model.
  • For any project evaluating machine learning models or algorithms is essential.

Frequently Asked Questions

Q1. What are the 3 metrics of evaluation?

A. Accuracy, confusion matrix, log-loss, and AUC-ROC are the most popular evaluation metrics.

Q2. What are evaluation metrics in machine learning?

A. Evaluation metrics quantify the performance of a machine learning model. It involves training a model and then comparing the predictions to expected values.

Q3. What are the 4 metrics for evaluating classifier performance?

A. Accuracy, confusion matrix, log-loss, and AUC-ROC are the most popular evaluation metrics used for evaluating classifier performance.

Q4.What are the most common evaluation metrics?

Classification: Accuracy, precision, recall, F1-score, confusion matrix, ROC curve, AUC.Regression: MAE, MSE, RMSE, R-squared.Other: Log loss, Cohen’s kappa, lift

Tavish Srivastava, co-founder and Chief Strategy Officer of Analytics Vidhya, is an IIT Madras graduate and a passionate data-science professional with 8+ years of diverse experience in markets including the US, India and Singapore, domains including Digital Acquisitions, Customer Servicing and Customer Management, and industry including Retail Banking, Credit Cards and Insurance. He is fascinated by the idea of artificial intelligence inspired by human intelligence and enjoys every discussion, theory or even movie related to this idea.

Responses From Readers

Vishwa
Vishwa

I think adding multilogloss , in this would be useful. It is a good matrix to identify better model in case of multi class classification.

venugopal
venugopal

Very useful

Mario
Mario

Hi, Great post thanks. Just in number one confusion matrix you miscalculated the negative predicted value. It is not 1.7% but 98.3%. Same for specificity (59.81% instead of 40.19%). Since you reuse this example for ROC, your curve is actually better. But anyways your argument still holds. Nicely presented.

venkata ramana reddy kandula
venkata ramana reddy kandula

Its a good information

Sanjay.S
Sanjay.S

Very informative and useful article.Thank you..

Tamara
Tamara

Considering provided confusion matrix, negative predicrive value is 951/967 or not? Is there an error in confusion matrix example or in formulas?

Rishabh0709
Rishabh0709

Hi Tavish, Thanks again , for this valuable article. It would be great , if along with this very informative explanation , you can also provide how to code it , preferably in R. Thanks.

Mac Lover
Mac Lover

Introduction p Predictive Modeling works on constructive feedback principle. You build a model. Get feedback from metrics, make improvements and …

Sudheer
Sudheer

Excellent article! thanks for the effort.

Sudheer
Sudheer

Excellent article! Thanks a lot!!

Sudhindra
Sudhindra

Can we all the list of Statistical Models and the application scenarios please for a novice person like me?

Jorge
Jorge

Are there metrics for non supervised models as k-means for example? Thanks!

Scott Bradshaw
Scott Bradshaw

Hi, can you further explain this "Lift is dependent on total response rate of the population"? Is this only applicable when you are able to correctly predict 100% of the 1st (or more) deciles?

Comments are Closed

Flash Card

What are Evaluation Metrics in Machine Learning?

Evaluation metrics are numbers used to measure how well a machine learning model is performing. They provide a way to see if a model is accurate and effective, helping data scientists and engineers understand its strengths and weaknesses. These metrics also make it easier to compare different models to find the one that works best for a specific task.
Some commonly used evaluation metrics include:
Accuracy: The percentage of correct predictions out of all predictions, often used in classification problems.
Precision and Recall: Precision measures how many of the positive predictions are actually correct, while recall measures how many actual positives were correctly predicted by the model.
F1 Score: A balance between precision and recall, useful when there is an uneven class distribution.
Mean Squared Error (MSE): Measures how close predicted values are to actual values, commonly used for regression problems.
ROC-AUC Score: Evaluates the performance of classification models at different threshold levels.

Quiz

Which evaluation metric is typically used to balance precision and recall in a classification model?

Flash Card

What is a confusion matrix, and how is it used in classification problems?

A confusion matrix is an N X N matrix, where N is the number of predicted classes, used in classification problems. For binary classification, it results in a 2 X 2 matrix with four combinations of predicted and actual values. It is useful for measuring precision-recall, specificity, accuracy, and AUC-ROC curves. The confusion matrix helps in understanding the performance of a classification model by showing the true positives, false positives, true negatives, and false negatives.

What is a confusion matrix, and how is it used in classification problems?

Quiz

What does a confusion matrix help you understand in classification problems?

Flash Card

How does the F1 Score help in evaluating classification models?

The F1 Score is the harmonic mean of precision and recall values for a classification problem. It is particularly useful when trying to balance precision and recall, especially in cases where both are equally important. The F1 Score provides a single metric that considers both false positives and false negatives, offering a more comprehensive view of model performance. It is beneficial in scenarios where the class distribution is imbalanced, and accuracy alone might be misleading.

How does the F1 Score help in evaluating classification models?

Quiz

Why is the F1 Score particularly useful in evaluating classification models?

Flash Card

What are Gain and Lift charts, and how are they constructed?

Gain and Lift charts are used to check the rank ordering of probabilities in classification models. The construction involves calculating probabilities for each observation and ranking them in decreasing order. Observations are divided into deciles, with each group containing about 10% of the observations. The response rate is calculated at each decile for responders, non-responders, and the total, helping to evaluate model effectiveness in targeting.

What are Gain and Lift charts, and how are they constructed?

Quiz

What is the primary use of Gain and Lift charts in classification models?

Flash Card

Explain the Kolmogorov-Smirnov (K-S) chart and its purpose in model evaluation.

The K-S chart measures the performance of classification models by assessing the degree of separation between positive and negative distributions. A K-S value of 100 indicates perfect separation, where one group contains all positives and the other all negatives. It helps in understanding how well a model distinguishes between different classes. The K-S statistic is particularly useful in binary classification problems to evaluate model discrimination power.

Explain the Kolmogorov-Smirnov (K-S) chart and its purpose in model evaluation.

Quiz

What does the Kolmogorov-Smirnov (K-S) chart assess in classification models?

Flash Card

What is the Area Under the ROC Curve (AUC-ROC), and why is it advantageous?

AUC-ROC is a popular evaluation metric that plots sensitivity (true positive rate) against (1-specificity) or false positive rate. It is advantageous because it is independent of the change in the proportion of responders. The AUC-ROC provides a single value to summarize the model's ability to distinguish between classes. It is widely used in binary classification problems to assess the trade-off between sensitivity and specificity.

What is the Area Under the ROC Curve (AUC-ROC), and why is it advantageous?

Quiz

Why is the AUC-ROC considered advantageous in model evaluation?

Flash Card

How does Log Loss differ from AUC-ROC in evaluating model performance?

Log Loss considers the predicted probabilities for determining a model’s performance, focusing on the accuracy of probability estimates. Unlike AUC-ROC, which only considers the order of probabilities, Log Loss penalizes incorrect predictions more heavily. It is the negative average of the log of corrected predicted probabilities for each instance. Log Loss is useful when the model's capability to predict higher probabilities for positive samples is crucial.

How does Log Loss differ from AUC-ROC in evaluating model performance?

Quiz

What aspect of model performance does Log Loss focus on?

Flash Card

What is Root Mean Squared Error (RMSE), and when is it used?

RMSE is a popular evaluation metric used in regression problems to measure the average magnitude of errors. It assumes errors are unbiased and follow a normal distribution, making it sensitive to outliers. RMSE is used to assess the accuracy of continuous predictions, with lower values indicating better model performance. It is important to remove outliers from the dataset before using RMSE to avoid skewed results.

What is Root Mean Squared Error (RMSE), and when is it used?

Quiz

In what type of problems is RMSE primarily used?

Flash Card

Describe the concept of Cross Validation and its significance in model evaluation.

Cross Validation is a technique used to assess the generalization capability of a model by testing it on unseen data. It involves splitting the dataset into training and validation sets multiple times, typically using K-Fold Cross-Validation. This method helps in identifying overfitting by ensuring the model performs well across different subsets of data. Cross Validation provides a robust estimate of model performance, reducing selection bias and making efficient use of available data.

Describe the concept of Cross Validation and its significance in model evaluation.

Quiz

What is the main purpose of Cross Validation in model evaluation?

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