P4-7.1 Feature Selection¶

Section ID: P4-7.1 Version: v2026.07.20

P4-6 looked at what criterion should be used for evaluation. Now the question moves one step earlier. Before changing an evaluation metric, you must first inspect what input will be given to the model in the first place. Feature selection is the starting point of that input design.

This Section deals with how should good features be chosen? Rather than explaining complex selection algorithms in depth, the goal is to fix the judgment criteria that should be checked first in actual work.

This Section explains the meanings of feature selection and feature space. The next Section continues the current context through this handle, and the basic criterion for deciding what should fill the input slots is connected again through this Section and the concept glossary.

Scope Of This Section¶

This Section answers the following questions.

• What is a feature, and why is input design important?
• Why should not everything be included just because a lot of data are available?
• What feature-selection criteria should a reader inspect first?
• How is feature selection different from preprocessing?

This Section first closes how to choose good features and why feature selection is an input-design problem. The sense of separating preprocessing types by input problem is revisited in the supplementary P4-7.3, and the comparison perspective between statistical-test-based selection and recursive feature elimination is organized again in supplementary P4-7.4. The big picture of dimensionality reduction continues in P4-18.1 and P4-18.2.

Goals Of This Section¶

• You can explain a feature as a form in which real-world information has been turned into model input.
• You can explain that feature selection is connected not only to performance numbers but also to leakage, cost, stability, and interpretability.
• You can use basic questions that distinguish features to discard first from features to keep first.
• You can explain that if preprocessing is refining the selected features, then feature selection is deciding which features should be adopted in the first place.

Learning Background¶

What Is A Feature?¶

In machine learning, a feature is what happens when a real-world object is turned into an input slot the model can read.

For example, a customer-churn prediction problem can be read like this.

A feature is not a literal copy of something that existed in the world. It is an input representation cut and shaped to make the problem easier to solve.

So even with the same data, a completely different model emerges depending on which slots are chosen as features.

However, this judgment becomes stable only after what counts as one sample row is fixed first. If it is unclear whether one row means one customer, one action, or a summary row of several time points, then the meaning of the features also shakes.

To say it a little more theoretically, a feature is the unit of an input variable. A model usually cannot understand one large answer all at once. Instead, it looks at several input variables together and finds repeating patterns in their combination. Each input variable in that combination is a feature.

For example, in a house-price prediction problem, area, number of rooms, and distance to the station can become features. In a spam-classification problem, occurrence of a particular word, whether there is an attachment, and sender-domain pattern can become features. In other words, features change by problem, and even the same reality is expressed through a different set of features depending on what question is being asked.

Main Learning Content¶

Why Feature Selection Matters First¶

Feature selection is not simply the job of reducing the number of columns. It is the job of deciding what information will be allowed to participate in model judgment.

The scikit-learn documentation explains that its feature-selection modules can be used to reduce unnecessary features or features with heavy noise, thereby improving performance and computational efficiency. This judgment becomes even more important especially in high-dimensional data.

The core reasons are the following four.

1. If there are many irrelevant inputs, the model may learn noise.
2. Too many inputs can increase learning and inference cost.
3. The more hard-to-explain inputs there are, the harder result interpretation can become.
4. If an input that will not be available at the future prediction point is included, data leakage can occur.

Feature selection is keeping good signals while reducing dangerous signals and unnecessary signals.

Put theoretically again, feature selection adjusts the following four things at the same time.

Read through this table, feature selection is closer not to simple data cleanup but to redefining the learning problem.

Feature Space And Representation¶

It is important to look at features one by one, but machine learning usually does not judge from only one feature. It looks at the input space created by several features together. This Section handles that as feature space.

For example, suppose there are only two features.

• visits_30d
• support_tickets_30d

Then one customer can be represented as one point, (visit count, inquiry count). If there are thousands of customers, those points gather and make one space. Inside that space, the model learns the tendency of points that churn, the grouping among similar points, and the shape of boundaries.

So feature selection is not only the job of reducing the number of columns. It is also the job of designing the input space the model will see.

flowchart TB
  A["reality<br/>people / products / events"]
  B["feature design<br/>choose measurable aspects"]
  C["feature space<br/>rows as points in input space"]
  D["model learning<br/>find pattern or boundary"]

  A --> B
  B --> C
  C --> D

This diagram shows that feature selection is not simply choosing columns. It is a process of deciding into what kind of input space reality will be translated. Even with the same reality, the patterns and boundaries the model learns can change completely depending on what features are drawn out.

The key point of this diagram is that what matters more than whether there are many or few features is from what perspective reality was cut to create the input space.

Even the same fact can become easier or harder for a model to learn depending on how it is turned into a representation.

For example, imagine representing customer activity.

The key point this table shows is that feature selection is connected not only to choosing original columns, but also to what unit of representation will be used to build the input.

This difference becomes clearer when raw logs are handled. Even if values appear as a time sequence, model input often does not use all those raw rows directly. Instead, feature candidates are frequently made by summarizing them again by action unit.

Here too, the order is raw time series -> one summarized row per action -> feature selection. Feature selection is the stage that comes after this order, and again decides which input slots in the summarized row will be kept.

What matters here is not memorizing a specific name. It is asking whether the data are being changed from raw values as they are into a reading unit more directly connected to the problem. In other words, feature selection is not only column removal. It is also the work of redesigning the representation as raw time series -> one summarized row per action -> model input slots.

Read in reverse, this also means that in a table where what one row means is still unclear, the discussion of feature selection should not be started too early.

This scene becomes more direct when read like the following.

This table is an example of several rows of raw time series being re-expressed into one row per action summary rows. Here columns such as control_mean, sensor_a_peak, and sensor_a_slope are not just computed values. They are already the result of a choice about what should remain as model input slots. In other words, feature selection is already connected to the question what will be left in what representation even before the later question which columns should be dropped?

Go one step further, and some features do not end with describing one action only. They also connect to input that compares recent state and a baseline.

What matters in this comparison table is not the delta number itself, but that the features built earlier are now being used again in an interpretation frame that compares the recent section and the baseline. So feature selection is not only input design for learning. It is also design of a comparison report that people will read.

However, this comparison table only shows the change signal first. It does not automatically settle the cause of that difference.

This flow becomes even clearer when seen in a short code example.

Problem situation:

• raw logs across several rows should be turned into action-level features, and those features should then be reused as comparison inputs between a recent section and a baseline

Input:

• a small raw-log table containing action_id, time_step, control_level, sensor_a, and sensor_b

Expected output:

• an action-level summary table
• a comparison table with recent average, baseline average, and deltas

Concept to check:

• a feature is representation design that turns raw logs into one-row input
• the same feature can be reused both for model input and for an operations comparison table

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# This example compares raw feature candidates and selected feature sets to design the model input space.
import pandas as pd

raw = pd.DataFrame(
    [
        ["A-101", 0, 0.20, 24.8, 0.3],
        ["A-101", 1, 0.35, 25.6, 0.8],
        ["A-101", 2, 0.55, 27.1, 1.5],
        ["A-102", 0, 0.18, 24.5, 0.2],
        ["A-102", 1, 0.32, 25.3, 0.7],
        ["A-102", 2, 0.60, 27.8, 1.6],
        ["A-103", 0, 0.22, 25.0, 0.4],
        ["A-103", 1, 0.36, 26.1, 0.9],
        ["A-103", 2, 0.54, 29.1, 1.8],
    ],
    columns=["action_id", "time_step", "control_level", "sensor_a", "sensor_b"],
)

summary = (
    raw.groupby("action_id")
    .agg(
        duration_steps=("time_step", "count"),
        control_mean=("control_level", "mean"),
        sensor_a_peak=("sensor_a", "max"),
        sensor_b_mean=("sensor_b", "mean"),
    )
    .reset_index()
)

recent = summary.tail(2).mean(numeric_only=True)
baseline = summary.head(len(summary) - 2).mean(numeric_only=True)

comparison = pd.DataFrame(
    {
        "recent": recent,
        "baseline": baseline,
        "delta": recent - baseline,
    }
)

print(summary)
print(comparison.round(2))

The first thing to read in this code is not a performance number. It is the transformation of the representation.

• several rows of raw logs are turned into an action-level feature table
• that feature table is again turned into a recent-versus-baseline comparison table
• so feature selection already decides what will become a comparable input before later preprocessing
• summary rows make comparison easier, but they do not fully replace all context in the raw time series

So feature selection always carries the following two questions together.

1. What information will be kept?
2. In what form of input representation will that information be kept?

The second question connects to preprocessing too, but the starting point is still feature design.

What Should A Good Feature Have?¶

People often misunderstand a good feature = a numeric feature. But what matters is not the data type. It is what kind of relationship that feature has with the problem.

This Section sees a good feature as an input that contains signal the model can learn from, while also being reproducible again in actual operations. In other words, it should not be a column that only looks convincing inside a training table. It should be an input that can still be used legitimately at prediction time, whose meaning does not shake, and whose role remains clear when placed beside other features.

Theoretically, the following five perspectives matter.

1. Does It Contain Signal?¶

That feature should contain some pattern related to the target label.

For example, in a churn problem, recent visit count may plausibly relate to churn. By contrast, a completely arbitrary internal serial number usually does not explain the cause or tendency of the problem.

A good feature should contain to some extent signal that helps predict the target.

2. Is The Noise Not Too Large?¶

Just because a value looks present does not automatically make it a good feature.

• Is the measurement itself inaccurate?
• Was it entered manually, so fluctuation is large?
• Does the meaning often change by situation?

In such a feature, the noise can become larger than the signal. Then the model becomes more likely to learn accidental fluctuation than a stable rule.

3. Can It Actually Be Used At Prediction Time?¶

It is not enough that a good feature is a value that exists inside the training dataset. It must also be a value that can be obtained at the actual prediction moment.

• Is it recorded only after the outcome appears?
• Is it a value that a human attached afterward as a judgment?
• Is it a summary value that can be calculated only after looking across the entire period?

For example, values such as contract_cancelled_at, refund_confirmed_at, and next_30d_spend may look like very strong signals in the training table, but at the actual prediction moment they are often still unknown. Even if the signal looks strong, such features are not good features; they are features with leakage risk.

So a good feature should bring not only help for prediction, but also legitimate usability at the actual prediction time.

4. Is Redundancy Not Excessive?¶

If too many features with similar meaning are included, the information may not become richer. The explanation may simply repeat itself.

For example, some redundancy may be suspected if the following columns appear together.

• monthly_spend
• quarterly_spend
• yearly_spend

Of course, all of them are not always unnecessary. But the first thing to check is whether similar meaning is just being repeated at different units.

5. Can It Be Recreated In Operations?¶

A good feature should not be a value that exists only in the offline experiment table. It must be reproducible by a similar rule in the actual service.

• Is the collection delay not too large?
• Is the quality not too unstable because it depends on manually written notes?
• Is it a value that is hard to fetch stably at inference time because of privacy, cost, or API dependence?

For example, a summary note written later by a support agent may look useful during training, but it may arrive too late for real-time churn prediction or fluctuate in format. By contrast, aggregate features such as visits_7d and failed_payments_30d are relatively easier to reproduce stably.

In summary, a good feature tends toward the following condition.

It contains signal related to the problem, is not overwhelmed by noise, can be used at prediction time, has manageable redundancy with other features, and can be recreated in operations.

These five conditions do not live separately. For example, even if a feature looks strong, it is disqualified if it cannot be used at prediction time. If it cannot be reproduced in operations, then it is not suitable as a service input. By contrast, even if the signal is not extremely strong, it can become a good basic feature candidate if it is collected repeatedly in a stable way and its meaning stays clear.

To grasp a good feature more intuitively, it helps to contrast it against a feature that looks attractive but is actually dangerous.

So a good feature is closer not to a feature that looks strong, but to a feature that can be reused legitimately and stably.

The same churn problem can be read like this.

The important point here is not to find the single feature with the strongest signal. It is to build a bundle of features that both the model and operations can sustain. For example, contract_cancelled_at may look extremely strong but still gets rejected, while visits_30d may remain even if it is not perfect. The reason is that the latter is more legitimate and repeatable.

If these five conditions are bundled once more, they can be organized like this.

flowchart TB
  A["candidate feature<br/>possible input signal"]
  B["signal<br/>related to target?"]
  C["noise<br/>stable enough?"]
  D["timing<br/>available at prediction time?"]
  E["redundancy<br/>not just repetition?"]
  F["operations<br/>reproducible in service?"]
  G["good feature candidate<br/>worth keeping"]

  A --> B
  B --> C
  C --> D
  D --> E
  E --> F
  F --> G

This diagram means that a good feature should not be read as a single mysterious property. It should be split into the five questions of relevance, stability, timing legitimacy, redundancy management, and operational reproducibility.

The Last Questions That Follow When Choosing A Good Feature¶

A good feature usually has to survive the following questions too.

1. If this feature disappears, does the model really miss an important signal?
2. If this feature exists but cannot be reproduced later in operations, what good is it?
3. Is this feature merely repeating what another feature is already saying, or does it add a new perspective?

Once those questions are attached, feature selection becomes closer not to gathering many columns but to designing explainable input.

Why You Should Not Include Everything Just Because It Is Available¶

In practice, the more columns a database has, the more dangerous it can become.

For example, imagine that a loan-review model has the following columns.

The key point this table shows is simple.

Feature selection is not a competition to include more. It is the job of choosing information that can be used legitimately at the prediction moment.

Three Questions Readers Should Use First¶

When starting feature selection, the order of questions matters more than a complex algorithm.

1. Can This Information Really Be Used At Prediction Time?¶

The first thing to inspect is timing.

• Is it a value that exists only after the result appears?
• Is it a judgment attached afterward by a human?
• Is it a value made after looking across the whole test dataset?

The common-pitfalls section of scikit-learn explains that preprocessing steps, including feature selection, should use only the training data, and that performance becomes optimistically inflated when the test data are pulled in.

This can be remembered like this.

If information that is unknown at prediction time is put into the input, the result is not a model that predicts well. It is a model that peeked at the answer beforehand.

2. Is This Information A Signal Related To The Problem?¶

The second question is relevance.

Not every column has the same weight.

• Is it a behavior record directly connected to the problem?
• Is it just a number that distinguishes a person or an object?
• Is it almost always the same value, so its information content is low?
• Is it almost repeating the same meaning as another column?

The scikit-learn documentation provides ways to reduce features with almost no variance, features that can be scored by univariate statistics, and features that can be reduced through model-based importance. But in this Section, the focus comes before algorithms. It is on understanding why could this be reduced?

3. Can This Information Be Obtained Stably In Operations?¶

The third question is operational reality.

Some features look present inside a training dataset but are hard to obtain stably every time in an actual service.

• Does collection delay happen often?
• Does quality fluctuate because a human must type it by hand?
• Is it difficult to use in operations because of privacy or cost?
• Does it increase inference-time latency because it has to be fetched at every model call?

In the end, feature selection is not only a data-science problem. It is also a service-design problem.

The key of this judgment flow is not an algorithm but the inspection order. In particular, it matters that the structure asks can this be used at prediction time? first.

How Are Feature Selection And Preprocessing Different?¶

Readers often mix the two together. But the questions are different.

Feature selection is the job of fixing the entrance, and preprocessing is the job of refining the chosen input so the model can read it well.

If that difference is drawn simply, it becomes the following.

flowchart TD
  A["raw columns<br/>all available fields"]
  B["feature selection<br/>keep / drop / postpone"]
  C["selected features<br/>usable input set"]
  D["preprocessing<br/>scale / fill / encode"]
  E["model input<br/>ready for training"]

  A --> B
  B --> C
  C --> D
  D --> E

What Should Be Kept First, And What Should Be Removed First?¶

Before complex algorithms, feature selection is also the job of making clear for what reason is something kept, and for what reason is it postponed or removed?

The order in this table matters. Does it look relevant? should come only after can it be used legitimately at prediction time? is checked. That way the legitimacy of the input is inspected before performance numbers.

Detailed Learning Content¶

Organizing The Meaning In An Academic Context¶

Introductory books often use variable and feature almost as if they were the same word. But in an academic context, they are sometimes separated slightly.

The classic survey paper by Guyon and Elisseeff distinguishes raw input variables from constructed features. At an introductory level, that difference can be translated like this.

For example, it can be read like this.

A feature is not only a column name. It points to the whole input representation fed into learning. So feature selection is sometimes used in a broad sense that includes both which original variables will remain and which constructed features will be adopted.

The purpose of feature selection is also organized a little more clearly in academic writing. Guyon and Elisseeff explain the purpose as the following.

1. improving prediction performance
2. building a faster and lower-cost predictor
3. helping understanding of the data-generating process

These three purposes continue directly into practice.

One more important point is that relevance and usefulness are not always the same word.

For example, if two columns contain almost the same information, both can still be relevant. But when building a predictor, just one may already be enough. Then the other becomes a feature that is relevant but less additionally useful.

This distinction shows that feature selection is not keeping every column that seems most relevant, but finding a subset that has low redundancy and is useful when used together.

Cases And Examples¶

Case 1. In A Churn-Prediction Table, More Columns Can Become More Dangerous¶

A subscription-service team is building a customer-churn prediction model. The signals people first used were things such as recent login count, inquiry frequency, whether there was payment failure, and history of plan changes.

But when the database is opened, there are far more columns available. It may look richer to include everything, from customer_id to contract completion timestamp, notes left later by an agent, and whether there will be delinquency next month. But among these are numbers that only distinguish people, values that exist only after the result happened, and information that would still be unknown at the actual prediction time.

In this scene, feature selection becomes not put in more but keep only signals that can be used legitimately. First inspect whether the value is available at the prediction moment, then check whether it contains a signal relevant to the problem, and then inspect whether it can be collected stably in operations. After that process, the table may look smaller on the surface, but in reality it creates a more generalizable input space.

The checkable result is clear as well. When the validation score is compared between the version that includes leakage columns and the version that excludes them, the reason the original high performance was an illusion can appear. And when the remaining feature list is reviewed, it becomes possible to explain which columns were actual behavior signals and which columns were post-outcome information.

flowchart TD
  A["many churn columns"]
  B["include IDs and after-the-fact fields"]
  C["score may look high from leakage"]
  D["keep only usable signals at prediction time"]
  E["remove IDs, labels, post-outcome fields"]
  F["recheck validation score"]
  G["explain why the kept feature list is safer"]

  A --> B --> C
  A --> D --> E --> F --> G

Cases And Examples¶

Candidates To Drop First Through Practical Heuristics¶

Before complex computation, the following features are worth checking first.

This list is not a mathematical formula. It is a checklist a reader can use immediately when opening a dataset for the first time.

Choosing Feature Candidates Through A Small Example¶

The following is a very small example that assumes a customer-churn problem.

The important point in this example is not include itself. It is whether the reader can explain why it was included or excluded.

Practice And Examples¶

Slowly Checking The Five Criteria Of A Good Feature¶

The five groups below are practice for reconnecting the earlier criteria in the order small scene -> question to ask first -> first judgment -> explanation. The point is not to memorize one answer, but to check whether you can explain one step at a time why this feature remains while that feature is postponed or removed.

At first, it helps not to choose an answer too quickly, but to separate the following two steps on purpose.

1. Why does this feature look attractive on the surface?
2. Even so, for what reason should it be kept, held, or removed?

If that judgment order is first compressed into a short flow, it looks like this.

flowchart TB
  A["candidate feature"]
  B["why does it look useful?"]
  C["five checks<br/>signal / noise / timing<br/>redundancy / operations"]
  D["keep"]
  E["hold"]
  F["remove"]

  A --> B
  B --> C
  C -->|clear and usable| D
  C -->|uncertain| E
  C -->|risky or invalid| F

This diagram is meant to hold on to the order say first why it looks attractive, then separate keep / hold / remove through the five criteria, rather than jump straight to the answer. The five exercises below slowly unpack that flow one by one.

1. Does It Contain Signal?¶

Example:

In a churn-prediction problem for an online course, compare the following two feature candidates.

Question to ask first:

• Which of the two features contains behavior change?
• Which one is easier to explain in words as connected to the problem?

In this scene, videos_completed_7d is the better candidate because recent learning behavior seems connected to churn. By contrast, user_id_suffix is numeric, but it does not explain a signal related to the problem.

Practice:

Choose which of the following two should remain earlier as a first-pass candidate, and write the judgment in two sentences.

Order to write:

1. On the surface, both are numbers, but which one is closer to a real behavior signal?
2. Why can the other be read as an identifier fragment rather than a generalized pattern?

Explanation:

failed_logins_14d should remain first. It can connect to an access problem or usability friction as a behavior signal. account_number_last_digit is closer to an identifier fragment, so it is hard to expect a generalized pattern from it.

2. Is The Noise Not Too Large?¶

Example:

Consider the following two features in a customer-complaint prediction problem.

Question to ask first:

• Who created this value?
• Even if the same situation happens again, can it be recorded repeatedly under a similar standard?

Both may still relate to complaints, but agent_feeling_score is more likely to have a shaky input standard. Such a feature can pull in more noise than signal.

Practice:

Choose which of the following is more likely to have larger noise, and explain why from the viewpoint of recording style.

Order to write:

1. Which feature has a more consistent recording format?
2. Which feature is harder to compare because different people may write the same meaning differently?

Explanation:

customer_mood_text has the larger noise risk. Even the same situation may be written differently by different people, and the recording format is not stable. Automatically recorded delay time has a more stable standard.

3. Can It Actually Be Used At Prediction Time?¶

Example:

In a payment-churn prediction problem, there are the following feature candidates.

Question to ask first:

• Is this value already known at the moment the prediction button is pressed?
• Or is it only fixed after the outcome has already happened?

refund_confirmed_at may look like a very strong signal in the training table, but at the actual prediction moment it may still be unknown. Such a value is not a good feature, but a feature with leakage risk.

Practice:

Choose which of the following two has higher timing legitimacy, and write also why the other one is dangerous.

Order to write:

1. Does one of the two values look at a future section?
2. Which one can actually be calculated immediately at the service prediction point?

Explanation:

Only spend_14d_before_prediction can be used legitimately. next_14d_spend is a value that brings in future information beforehand, so it cannot be used as model input.

4. Is Redundancy Too Excessive?¶

Example:

Suppose the following columns are being inserted all at once into a purchase-churn prediction problem.

Question to ask first:

• Do these features show different behaviors?
• Or are they repeating the same behavior while only changing the period?

This does not mean that all three columns are useless. But if they are repeating the same spending scale with only different periods, then information may not really be increasing. The same explanation may simply be inserted several times.

Practice:

Choose which of the following two groups needs redundancy review more urgently, and distinguish whether it is repetition of the same role or a combination of different roles.

Order to write:

1. What do the three values in the first group end up talking about in common?
2. Why can the second group be read as combining different viewpoints?

Explanation:

The first group should be checked for redundancy first. It repeats features of the same role with only different periods. The second group shows different behavioral aspects, so the roles are more separated.

5. Can It Be Recreated In Operations?¶

Example:

In a real-time recommendation model, there are the following feature candidates.

Question to ask first:

• Can this feature be recreated again in the same way at each inference point?
• Is there a risk that it will often be missing or late because of cost, delay, or manual input?

manual_quality_note may look plausible in the training table, but it may fail to arrive in time at each actual inference point. Such a feature has low operational reproducibility.

Practice:

Choose which of the following has higher operational reproducibility, and explain it separately from the viewpoints of collection stability and operational cost.

Order to write:

1. Which feature shakes more because of failures or external dependence?
2. Which feature can be recalculated more cheaply and stably at each inference point?

Explanation:

sessions_1h is the safer candidate. It can be repeatedly calculated from internal logs. api_partner_score may shake at inference time because of cost, delay, and failures.

Inspecting Feature Candidates In A First Pass Through A Python Example¶

The example below imitates a very simple first pass often done in practice. It first catches identifiers, the label column, columns created after the outcome, and constant columns.

Problem situation:

• when feature candidates are opened for the first time, it may not be immediately clear what should remain as input and what should be excluded

Input:

• a list of customer rows rows

Expected output:

• include/exclude judgment for each column
• the reason for that judgment

Concept to check:

• the first step of feature selection is to inspect candidate columns by risk signal before model learning
• it is safer to read identifiers, labels, and post-outcome values as exclusion candidates first

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# This example compares raw feature candidates and selected feature sets to design the model input space.
rows = [
    {
        "customer_id": "C001",
        "visits_30d": 12,
        "support_tickets_30d": 0,
        "contract_cancelled_at": "",
        "membership_tier": "gold",
        "country": "KR",
        "churn_next_month": 0,
    },
    {
        "customer_id": "C002",
        "visits_30d": 3,
        "support_tickets_30d": 2,
        "contract_cancelled_at": "2026-05-14",
        "membership_tier": "gold",
        "country": "KR",
        "churn_next_month": 1,
    },
    {
        "customer_id": "C003",
        "visits_30d": 7,
        "support_tickets_30d": 1,
        "contract_cancelled_at": "",
        "membership_tier": "gold",
        "country": "KR",
        "churn_next_month": 0,
    },
]

target = "churn_next_month"
columns = list(rows[0].keys())

selected = []
rejected = []

for column in columns:
    values = [row[column] for row in rows]
    unique_count = len(set(values))

    if column == target:
        rejected.append((column, "label"))
    elif column.endswith("_id"):
        rejected.append((column, "identifier"))
    elif column.endswith("_at"):
        rejected.append((column, "post-outcome timestamp"))
    elif unique_count == 1:
        rejected.append((column, "constant value"))
    else:
        selected.append((column, "keep as candidate"))

print("selected candidates:")
for name, reason in selected:
    print("-", name, "->", reason)

print()
print("rejected candidates:")
for name, reason in rejected:
    print("-", name, "->", reason)

The output is as follows.

 1
 2
 3
 4
 5
 6
 7
 8
 9
10

selected candidates:
- visits_30d -> keep as candidate
- support_tickets_30d -> keep as candidate

rejected candidates:
- customer_id -> identifier
- contract_cancelled_at -> post-outcome timestamp
- membership_tier -> constant value
- country -> constant value
- churn_next_month -> label

This output does not mean good features were fully determined. It only shows what should be suspected first when the reader opens a data table for the first time.

Supplement To The Detailed Learning Content¶

What Feature-Selection Methods Are Commonly Seen In scikit-learn?¶

In practice, the following methods appear often.

The goal of this Section is not to memorize those algorithms. It is to first fix can the reason for selection be explained? Algorithms are only tools that help in the next stage.

Checklist¶

• Are you first checking whether this column actually exists at prediction time?
• Even if a feature seems to contain signal, are you marking separately features that cannot be collected stably in operations?
• Can you explain feature selection and preprocessing separately as what will remain and how it will be transformed?
• Are you removing columns that are unknown at prediction time?
• Are the label itself or post-label information kept out of the input?
• Were identifiers and constant columns inspected first?
• Are only features that can be obtained stably in the real service being kept?
• Are feature selection and preprocessing not being mixed together as if they were one task?
• Can you explain that a feature is an expression that turns real-world information into model input, and that feature selection is closer to choosing input that can be used legitimately than to putting in more?
• Can you explain why leakage, relevance, and operational usability should be checked first?

Sources And References¶

• scikit-learn, 1.13. Feature selection, scikit-learn User Guide, accessed 2026-06-26. https://scikit-learn.org/stable/modules/feature_selection.html
• scikit-learn, 12.2. Data leakage during pre-processing, scikit-learn User Guide, accessed 2026-06-26. https://scikit-learn.org/stable/common_pitfalls.html