# Proving the Convexity of Log-Loss for Logistic Regression

Last Updated on February 25, 2023 by Editorial Team

**Author(s): Towards AI Editorial Team**

Originally published on Towards AI.

#### Unpacking Log Loss Error Function’s Impact on Logistic Regression

**Author(s):** Pratik Shukla

“Courage is like a muscle. We strengthen it by use.” — Ruth Gordo

#### Table of Contents:

- Proof of convexity of the log-loss function for logistic regression
- A visual look at BCE for logistic regression
- Resources and references

**Introduction**

In this tutorial, we will see why the log-loss function works better in logistic regression. Here, our goal is to prove that the log-loss function is a convex function for logistic regression. Once we prove that the log-loss function is convex for logistic regression, we can establish that it’s a better choice for the loss function.

Logistic regression is a widely used statistical technique for modeling binary classification problems. In this method, the log-odds of the outcome variable is modeled as a linear combination of the predictor variables. To estimate the parameters of the model, the maximum likelihood method is used, which involves optimizing the log-likelihood function. The log-likelihood function for logistic regression is typically expressed as the negative sum of the log-likelihoods of each observation. This function is known as the log-loss function or binary cross-entropy loss. In this blog post, we will explore the convexity of the log-loss function and why it is an essential property in optimization algorithms used in logistic regression. We will also provide a proof of the convexity of the log-loss function.

#### Proof of convexity of the log-loss function for logistic regression:

Let’s mathematically prove that the log-loss function for logistic regression is convex.

We saw in the previous tutorial that a function is said to be a convex function if its second derivative is >0. So, here we’ll take the log-loss function and find its second derivative to see whether it’s >0 or not. If it’s >0, then we can say that it is a convex function.

Here we are going to consider the case of a single trial to simplify the calculations.

#### Step — 1:

The following is a mathematical definition of the binary cross-entropy loss function (for a single trial).

Figure — 1: Binary Cross-Entropy loss for a single trial

#### Step — 2:

The following is the predicted value (ŷ) for logistic regression.

Figure — 2: The predicted probability for the given example

#### Step — 3:

In the following image, z represents the linear transformation.

Figure — 3: Linear transformation in forward propagation

#### Step — 4:

After that, we are modifying Step — 1 to reflect the values of Step — 3 and Step — 2.

Figure — 4: Binary Cross-Entropy loss for logistic regression for a single trial

#### Step — 5:

Next, we are simplifying the terms in Step — 4.

Figure — 5: Binary Cross-Entropy loss for logistic regression for a single trial

#### Step — 6:

Next, we are further simplifying the terms in Step — 5.

Figure — 6: Binary Cross-Entropy loss for logistic regression for a single trial

#### Step — 7:

The following is the quotient rule for logarithms.

Figure — 7: The quotient rule for logarithms

#### Step — 8:

Next, we are using the equation from Step — 7 to further simplify Step — 6.

Figure — 8: Binary Cross-Entropy loss for logistic regression for a single trial

#### Step — 9:

In Step — 8, the value of log(1) is going to be 0.

Figure — 9: The value of log(1)=0

#### Step — 10:

Next, we are rewriting Step — 8 with the remaining terms.

Figure — 10: Binary Cross-Entropy loss for logistic regression for a single trial

#### Step — 11:

The following is the power rule for logarithms.

Figure — 11: Power rule for logarithms

#### Step — 12:

Next, we will use the power rule of logarithms to simplify the equation in Step — 10.

Figure — 12: Applying the power rule

#### Step — 13:

Next, we are replacing the values in Step — 10 with the values in Step — 12.

Figure — 13: Using the power rule for logarithms

#### Step — 14:

Next, we are substituting the value of Step — 13 into Step — 10.

Figure — 14: Binary Cross-Entropy loss for logistic regression for a single trial

#### Step — 15:

Next, we are multiplying Step — 14 by (-1) on both sides.

Figure — 15: Binary Cross-Entropy loss for logistic regression for a single trial

#### Finding the First Derivative:

#### Step — 16:

Next, we are going to find the first derivative of f(x).

Figure — 16: Finding the first derivative of f(w)

#### Step — 17:

Here we are distributing the partial differentiation sign to each term.

Figure — 17: Finding the first derivative of f(w)

#### Step — 18:

Here we are applying the derivative rules.

Figure — 18: Finding the first derivative of f(w)

#### Step — 19:

Here we are finding the partial derivative of the last term of Step — 18.

Figure — 19: Finding the first derivative of f(w)

#### Step — 20:

Here we are finding the partial derivative of the first term of Step — 18.

Figure — 20: Finding the first derivative of f(w)

#### Step — 21:

Here we are putting together the results of Step — 19 and Step — 20.

Figure — 21: Finding the first derivative of f(w)

#### Step — 22:

Next, we are rearranging the terms of the equation in Step — 21.

Figure — 22: Finding the first derivative of f(w)

#### Step — 23:

Next, we are rewriting the equation in Step — 22.

Figure — 23: Finding the first derivative of f(w)

#### Finding the Second Derivative:

#### Step — 24:

Next, we are going to find the second derivative of the function f(x).

Figure — 24: Finding the second derivative of f(w)

#### Step — 25:

Here we are distributing the partial derivative to each term.

Figure — 25: Finding the second derivative of f(w)

#### Step — 26:

Next, we are simplifying the equation in Step — 25 to remove redundant terms.

Figure — 26: Finding the second derivative of f(w)

#### Step — 27:

Here is the derivative rule for 1/f(x).

Figure — 27: The derivative rule for 1/f(x)

#### Step — 28:

Next, we are finding the relevant term to plug-in in Step — 27.

Figure — 28: Value of p(w) for derivative of 1/p(w)

#### Step — 29:

Here we are finding the partial derivative term for Step — 27.

Figure — 29: Value of p’(w) for derivative of 1/p(w)

#### Step — 30:

Here we are finding the squared term for Step — 27.

Figure — 30: Value of p(w)² for derivative of 1/p(w)

#### Step — 31:

Here we are putting together all the terms of Step — 27.

Figure — 31: Calculating the value of the derivative of 1/p(w)

#### Step — 32:

Here we are simplifying the equation in Step — 31.

Figure — 32: Calculating the value of the derivative of 1/p(w)

#### Step — 33:

Next, we are putting together all the values in Step — 26.

Figure — 33: Finding the second derivative of f(w)

#### Step — 34:

Next, we are further simplifying the terms in Step — 33.

Figure — 34: Finding the second derivative of f(w)

Alright! So, now we have the second derivative of the function f(x). Next, we need to find out whether this will be >0 for all the values of x or not. If it is >0 for all the values of x, then we can say that the binary cross-entropy loss is convex for logistic regression.

As we can see that the following terms from Step — 34 are always going to be ≥0 because the square of any number is always ≥0.

Figure — 35: The square of any term is always ≥0 for any value of x

Now, we need to determine whether or not the value of e^(-wx) is >0. To do that, let’s first find the range of the function e^(-wx) in the domain [-∞,+∞]. To further simplify the calculations, we will consider the function e^-x instead of e^-wx. Please note that scaling a function does not change the range of the function if the domain is [-∞,+∞]. Let’s first plot the graph of e^-x to understand its range.

Figure — 36: Graph of e^-x for the domain of [-10, 10]

From the above graph we can derive the following conclusion:

- As the value of x moves towards negative infinity (-∞), the value of e^-x moves towards infinity (+∞).

Figure — 37: The value of e^-x as x approaches -∞

2. As the value of x moves towards 0, the value of e^-x moves towards 1.

Figure — 38: The value of e^-x as x approaches 0

3. As the value of x moves towards positive infinity (+∞), the value of e^-x moves towards 0.

Figure — 40: The value of e^-x as x approaches +∞

So, we can say that the range of the function f(x)=e^-x is [0,+∞]. Based on the calculations, we can say that the function f(x)=e^-wx is always going to be ≥0.

Alright! So, we have concluded that all the terms of the equation in Step — 34 are≥0. Hence, we can say that the function f(x) is a convex function for logistic regression.

#### Important Note:

If the value of the second derivative of the function is 0, then there is a possibility that the function is neither concave nor convex. But, let’s not worry too much about it!

#### A Visual Look at BCE for Logistic Regression:

The binary cross entropy function for logistic regression is given by…

Figure — 41: Binary Cross Entropy Loss

Now, we know that this is a binary classification problem. So, there can be only two possible values for Yi (0 or 1).

#### Step — 1:

The value of cost function when Yi=0.

Figure — 42: Binary Cross Entropy Loss when Y=0

#### Step — 2:

Figure — 43: Binary Cross Entropy Loss when Y=1

Now, let’s consider only one training example.

#### Step — 3:

Now, let’s say we have only one training example. It means that n=1. So, the value of the cost function when Y=0,

Figure — 44: Binary Cross Entropy Loss for a single training example when Y=0

#### Step — 4:

Now, let’s say we have only one training example. It means that n=1. So, the value of the cost function when Y=1,

Figure — 45: Binary Cross Entropy Loss for a single training example when Y=1

#### Step — 5:

Now, let’s plot the function graph in Step — 3.

Figure — 46: Graph of -log(1-X)

#### Step — 6:

Now, let’s plot the function graph in Step — 4.

Figure — 47: Graph of -log(X)

#### Step — 7:

Let’s put the graphs in Step — 5 and Step — 6 together.

Figure — 48: Graph of -log(1-X) and -log(X)

The above graphs follow the definition of the convex function (“A function of a single variable is called a convex function if no line segments joining two points on the graph lie below the graph at any point”). So, we can say that the function is convex.

**Conclusion:**

In conclusion, we have explored the concept of convexity and its importance in optimization algorithms used in logistic regression. We have demonstrated that the log-loss function is convex, which implies that its optimization problem has a unique global minimum. This property is crucial for ensuring the stability and convergence of optimization algorithms used in logistic regression. By proving the convexity of the log-loss function, we have shown that the optimization problem in logistic regression is well-posed and can be efficiently solved using standard convex optimization methods. Moreover, our proof provides a deeper understanding of the mathematical foundations of logistic regression and lays the groundwork for further research and development in this field.

**Citation:**

For attribution in academic contexts, please cite this work as:

Shukla, et al., “Proving the Convexity of Log Loss for Logistic Regression”, Towards AI, 2023

#### BibTex Citation:

@article{pratik_2023,

title={Proving the Convexity of Log Loss for Logistic Regression},

url={https://pub.towardsai.net/proving-the-convexity-of-log-loss-for-logistic-regression-49161798d0f3},

journal={Towards AI},

publisher={Towards AI Co.},

author={Pratik, Shukla},

editor={Binal, Dave},

year={2023},

month={Feb}

}

Proving the Convexity of Log-Loss for Logistic Regression was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

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