Vivek Thakur

If you’ve dealt with estate agents in London, you know the drill. They move fast, answer calls late in the evening, arrange viewings whenever needed, and chase deals aggressively. Most of them are heavily commission-driven, which means every sale matters.

Then you start looking for property in the English countryside.

And suddenly it feels like you’ve stepped into a different industry entirely.

The biggest surprise is that even the same big brands behave very differently outside the city. Most London based real esatte firms have sharp, motivated teams in the city. But once you start dealing with some countryside branches, the experience can be surprisingly sluggish.

The urgency disappears. Emails take days to get responses. Calls go unanswered. And arranging viewings can feel oddly difficult.

One of the more amusing quirks is the viewing schedule. Many countryside agents prefer weekday viewings and are reluctant about Saturdays. Which is ironic, because most buyers with jobs are actually free on weekends. The worst agents, in my honest opinion, are Knight Frank Countryside Department and Butler Sherborn. As a property owner, I will never list my property with them.

Just because they are on salaries, and they know some properties will sell on their own, they are super slow and uninterested. They know they will get a lot of listings because of their brand name, so they are not willing to give individual attention to owners. Instead of adapting to buyers’ schedules, the expectation sometimes seems to be that buyers should adapt to the agent’s calendar.

The root of the difference is fairly simple. City agents often operate in a highly commission-driven environment. Their income depends heavily on closing deals, so they move quickly and push hard.

Many countryside agents, on the other hand, appear to operate more like traditional salaried roles. When the financial incentive is weaker, the urgency tends to follow.

None of this is catastrophic. It’s just unnecessarily inefficient.

For sellers, this difference matters more than most people realise. A motivated agent will push viewings, chase buyers, and create momentum. A passive agent simply lists the property and waits.

The countryside property market itself is wonderful. Beautiful homes, great land, peaceful surroundings.

The agent experience, however, can sometimes feel like it’s still running on a much slower clock.

The lesson is simple. Don’t assume the brand name guarantees the same level of energy everywhere. In property, the individual agent and the culture of the local office matter far more than the logo on the signboard.

When I first started with AI, I used to get confused between bias and variance. It felt like one of those abstract concepts that everyone talked about but never really explained in a way that stuck.

Then, one day, I stumbled upon this cooking analogy (which I tried to explain in my own language below), and everything clicked.

Training, Validation, and Test Data: A Cooking Story

Imagine you’re preparing for a cooking competition. You’re handed a set of ingredients and need to create the perfect dish. But how do you make sure it turns out great every single time?

This is where training, validation, and test data come into play. Think of it as the difference between practicing, taste-testing, and the final judging.

Training Set – The Practice Kitchen

A chef doesn’t just walk into a competition and wing it. They practice: experimenting with different ingredients, testing techniques, and refining their skills.

In AI, the training set is the data the model learns from. It’s the hours spent tweaking the recipe, adjusting seasoning, and figuring out what works and what doesn’t. The model (or chef, or for RL folks: the policy :P) keeps refining its approach based on what it has already seen.

If the chef doesn’t practice enough, they’ll be clueless when the competition starts. But if they practice only one recipe over and over without considering variations, they might struggle when faced with a different set of ingredients. That’s the risk of overfitting – being too dependent on what was seen before.

Validation Set – The Taste Test

A smart chef doesn’t just rely on their own taste buds. They invite friends over to try the dish and give feedback. Maybe it’s too salty, maybe it needs more spicy. The friend’s feedback matters a lot!

And this is exactly what the validation set does. It’s a smaller, separate portion of data that helps fine-tune the model, making sure it generalizes well rather than just memorizing patterns from the training set.

If a chef only listens to their own opinion, they risk being biased. If an AI model only focuses on training data without external feedback, it risks being too rigid.

Test Set – The Final Judging

Now comes competition day. The chef presents their dish to the judges, who have never tasted it before. There are no second chances – this is the moment of truth!

In AI, the test set is where we check how well the model performs on completely new data. It’s an unbiased evaluation, similar to the judges scoring a dish based purely on how it tastes in that moment.

But what if you’re working with a small dataset or doing minimal fine-tuning? Then you often don’t need a separate test set. If all you’re doing is making sure the model isn’t overfitting, just training and validation are enough, like adjusting a dish based on close friends’ feedback rather than a formal panel of judges.

Summary?

Training Set → Learns patterns by adjusting weights.

Validation Set → Fine-tunes the model by adjusting hyperparameters & weights.

Test Set → Final evaluation, no weight adjustments.

Bias vs. Variance: Two Types of Chefs

Beyond training and validation, one of the biggest challenges in AI modeling is finding the right balance between bias and variance. Let’s break this down with another cooking example.

Bias: The Overly Simplistic Chef

Meet Gina. Gina believes every dish should taste good with just salt and pepper. No matter the cuisine, no matter the occasion, that’s the only trick up Gina’s sleeve.

Italian? Salt and pepper. Thai food? Salt and pepper. Sushi? You guessed it! Salt and pepper.

Sometimes the dish turns out fine, but most of the time, it lacks complexity and depth. This is high bias in AI – the model is too simplistic and fails to capture the full complexity of the problem. It’s like an undertrained model that assumes every situation can be solved with the same basic rule.

A high-bias model underfits the data, meaning it doesn’t learn enough from the training examples.

Variance: The Overly Reactive Chef

Now, meet Tiki. Tiki is the opposite. If a guest says the dish is too spicy, Tiki removes all the spice. If the next guest says it’s too bland, Tiki overcompensates by adding too much seasoning. Every small feedback or comment results in a massive adjustment.

The result? No two dishes taste the same. Some turn out great, others are a disaster.

This is high variance in AI—the model is too sensitive to training data and struggles to generalize. It overfits, meaning it performs well on known examples but fails when faced with anything slightly different.

Finding the Right Balance

A great AI model, just like a great chef, needs balance. It should learn enough to recognize important patterns (low bias) but not be so reactive that it becomes unpredictable (low variance). Another example in terms of a student learning some subjects:

High Bias: A student who memorizes only one simple trick and never digs into the deeper concepts.
High Variance: A student who over-memorizes every tiny detail from practice tests and gets thrown off by even a slight change in exam questions.
Low Bias: Like a student who learns the underlying concepts instead of just one trick.
Low Variance: Like a student who can apply their understanding flexibly to new problems.

My fav fun example:

High Bias: Imagine a superhero who only uses one superpower, say flying, for every challenge, so when a problem needs super strength or speed, he/she just can’t handle it.
High Variance: Now picture a superhero who tries to use every superpower at once in every situation, switching all the time and never mastering any, which makes his/her actions unpredictable.

Getting this balance right isn’t about using fancy algorithms alone. It’s about understanding the data, fine-tuning based on feedback, and knowing when to stop adjusting!

Happy coding!