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The following visual certainly delivers in terms of eye candy:

enter image description here

There was no details on the model specification, but let's just assume its something like:

$$price = \beta_{0} + \beta_{1} x_{surface} + \beta_{2} x_{cyear} + \epsilon$$

Where:

  • $price$ - the selling price of a house
  • $x_{surface}$ - the surface area of the house
  • $x_{cyear}$ - the year the house was constructed

Edit

After much effort, I found a way to iterate through the domain of the variables age and area using coefficients to get prediction values. I left out residuals for simplicity. Here is my code:

c = 20595
area = 39.28
age = -402.5
master_array = []
step = 50
x_range = 3800
y_range = 50
for i in range(step):
    for j in range(step):
        temp = {}
        temp['x'] = i*(x_range/step)
        temp['y'] = j*(y_range/step)
        temp['pred'] = c+((i*x_range/step)*area+(j*y_range/step)*age)
        master_array.append(temp)

I have made modest progress. The constant and coefficients age and area I obtained after I ran a regression using the above specification on a data set from my econometrics text book. Clearly, the output should not be expected to be exactly the same as the inspiration visual, but I didn't get anywhere near the spread of prediction regions. Mine looks simply like a gradient. Output below, age is y axis, area is x axis, light blue=cheap house, dark blue= expensive house:

enter image description here

I'm concerned there is a flaw in my code. Quick inspection led me to notice there appears to be no negative relationship of age shown in the plot (we would expect more darker blue regions where age is close to 0. Maybe someone with experience with this type of visual can advise on my implementation.

Question

Can we expect OLS estimation to only have a 'boring' linear gradient for prediction regions, as seen in my visual, or maybe it's user error on my part? Either way, what type of estimation could explain the 'cool' / 'interesting' prediction zones seen in the inspiration visual?

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  • 2
    $\begingroup$ Could you add what you want to use the figure for? It took me some time to understand what I was looking at, and even now I have trouble seeing what, if any, trend in the residuals is visible. It might be better to make the residuals' character expansion factor proportional to their size. As it is now it is hard to contrast the residuals against the predicted value. $\endgroup$ Apr 3, 2020 at 10:06
  • $\begingroup$ For answering the question whether there really is no negative relationship between age and price you should draw a scatterplot of the data (not of the prediction of a model that you train on the data) first. Only if there is the relationship 'new houses are more expensive' present in the data, a model can figure that out. Then you should go for the model as a second step. $\endgroup$ Apr 30, 2020 at 6:40
  • $\begingroup$ Your post is completely unclear. Where is the data to reproduce whatever it is you are looking for? Please include a working example. Also, if you want to create "non-boring" uncertainty intervals, use (e.g.) seaborn's regplot. See my answer below. $\endgroup$
    – Yair Daon
    May 6, 2020 at 16:17

3 Answers 3

5
+50
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There is probably nothing wrong with your code, but it's hard to tell as it is not complete and reproducible.

Age has a much smaller influence on the price than area: For the largest house, the price, if it were new, would be around 170,000. If it were 50 years old, the price would still be around 150,000. You can see it better if you use a diverging color map:

House prices

This image was generated by the following code:

import numpy as np
import matplotlib.pyplot as plt
c       = 20595.
area    =    39.28
age     =  -402.5
step    =    50
x_range =  3800
y_range =    50
dy, dx = y_range/step, x_range/step

x, y = np.mgrid[slice(0, x_range + dx, dx),
                slice(0, y_range + dy, dy)]
z = c + x*area + y*age
z = z[:-1, :-1]
z_min, z_max = 0, np.abs(z).max()

fig, ax = plt.subplots()
qm = ax.pcolormesh(x, y, z, cmap='RdBu', vmin=z_min, vmax=z_max)
ax.set_title('Prices')
fig.colorbar(qm, ax=ax)
plt.show()

Answer:

As long as your predictor terms are all linear, the boundary is bound to be "boring". If you want an "interesting" boundary, you need to introduce non-linearities, like

z = 1e5*(np.sin(2*x/x_range) + np.cos(2*y/y_range)+1)**2
z = z[:-1, :-1]
z_min, z_max = 0, np.abs(z).max()

fig, ax = plt.subplots()
qm = ax.pcolormesh(x, y, z, cmap='RdBu', vmin=z_min, vmax=z_max)
ax.set_title('Prices')
fig.colorbar(qm, ax=ax)
plt.show()

Nonlinear house prices

The image you quote in your question likely depicts the prediction by a non-linear model, perhaps random forest or neural network.

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  • $\begingroup$ Very interesting, thank you. I am familiar with adding quadratic terms to capture non-linearities, but have not seen trig functions before. May ask a separate question on that at some point. $\endgroup$ May 6, 2020 at 7:45
  • $\begingroup$ Also, would you mind adding the code for the second (trig function) plot? I tried updating z and just calling the plot again as for the linear model plot, but I don't think that gave me the right output. $\endgroup$ May 6, 2020 at 7:55
1
$\begingroup$

I'm concerned there is a flaw in my code. Quick inspection led me to notice there appears to be no negative relationship of age shown in the plot (we would expect more darker blue regions where age is close to 0. Maybe someone with experience with this type of visual can advise on my implementation.

About the true relationship:

In the answer below I assume that your question relates to the apartments dataset from the DALEX package. Your dataset might be slightly different, but I guess that it will still work

You are right that there is not much influence from the age when you fit with only a linear term.

Note that the dataset is specifically generated to relate to the effect of Anscombe's quartet (It is artificial data).

From the R documentation for the DALEX package:

Structure of the dataset is copied from real dataset from PBImisc package, but they were generated in a way to mimic effect of Anscombe quartet for complex black box models.

And the true relation is:

$$\begin{array}{rcl} \text{price} &=& 5000 + 600 \cdot \underbrace{(\vert \text{year}-1965 \vert > 30)}_{\llap{\text{this is a logical variable}}\rlap{\text{ with values 0 or 1}}} \\ && - 10 \cdot \text{surface} - 100 \cdot \text{floor} - 50 \cdot n_{\text{rooms}} + 1.5 \cdot \text{district} \end{array}$$

So, while there is nearly zero correlation between price and age, there is still some some sort of quadratic relationship between price and age.

When you fit a linear model with only a linear term for age then the coefficient will be close to zero. But with a quadratic term for age you should get some curved function.

Examples of fits with different models

Linear models

$$\mathbf{\text{price} = a + b \cdot \text{surface} + c \cdot \text{year}}$$

simple linear model

$$\mathbf{\text{price} = a + b \cdot \text{surface} + c \cdot \text{year} + d \cdot \text{year}^2}$$

linear model with quadratic term

$$\mathbf{\text{price} = a + b \cdot \text{surface} + c \cdot (\vert\text{year} - 1965\vert > 30)}$$

linear model with logic term

Random forest model:

random forest model

Support Vector Regression

support vector regression

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As I mentioned in a comment above, it is hard to understand what you want. If you want diverging confidence intervals, see code below.

import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt

plt.close('all')
x = [t for t in np.linspace(0, 5,num=20)]
x.append(22)
x.append(-11)
x = np.array(x)
alpha, beta = 223, 2.34
y = alpha + beta*x + np.random.normal(loc=0,scale=0.4, size=x.shape)
y[-2] = y[-2] - 15
y[-1] = y[-1] - 25
df = pd.DataFrame(data=np.vstack([x,y]).T, columns=['x', 'y'])
sns.regplot(
    x='x',
    y='y',
    data=df)
plt.show()

The blue line is calculated using OLS. Confidence intervals are drawn using bootstrap - sampling the data with replacement and fitting a line on sampled data using OLS.

image

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  • $\begingroup$ Could you add the output of 'plt.show()' and explain what 'sns.regplot' does (what sort of regression/fitting is it). $\endgroup$ May 6, 2020 at 17:35
  • $\begingroup$ @SextusEmpiricus Sure $\endgroup$
    – Yair Daon
    May 6, 2020 at 20:35
  • $\begingroup$ but that is not a heatmap. $\endgroup$ May 6, 2020 at 21:05
  • $\begingroup$ I know, I suggest an alternative. $\endgroup$
    – Yair Daon
    May 6, 2020 at 21:50
  • $\begingroup$ How does this alternative make sense, with relation to the linear/boring gradient of heat maps when simple OLS is used. This alternative is a plot for a function of a single variable with a confidence interval added to it, that's an entirely different thing. $\endgroup$ May 7, 2020 at 6:46

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