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Friendship promotion: TreeMind tree map mind map

Note: The following content is not original, it is provided by the developer, it is purely free to help, and does not endorse the product.

According to surveys, product managers who use mind maps are more likely to stand out in the workplace, with a success rate as high as 80%!

In this era of information explosion, how to efficiently organize, collate, mine and apply knowledge has become a challenge for every product manager.

Faced with this challenge, mind maps have become the secret weapon of high salaries for managers of large factories.

The TreeMind tree map is an accelerator in the mind map. It is equipped with an artificial intelligence model comparable to GPT. AI can generate a logical and hierarchical mind map with a single sentence, making the work of product managers easier.

XNUMX. Product Introduction

AsThe first batch of "AIGC+Mind Mapping" platforms, TreeMind tree diagramA detailed mind map can be generated in one sentence, opened a new era of AI-generated mind maps, which can quickly improve your learning and work efficiency.besidesMillions of Templates, to meet the needs of all walks of life and different segments, it is a real "encyclopedia" of mind mapping.

Click here to experience:

9. XNUMX core functions

  • AI word generates mind map
  • AI smart assistant asks questions at any time, maximizing productivity
  • Millions of templates Choose from 1,132,200 templates, new ones are added every day
  • Abundant material types Massive design resource library
  • Cross-platform file synchronization can be viewed anytime, anywhere
  • The team space supports multiple people to manage the team at the same time
  • Break away from the PPT and directly demonstrate in the tree diagram
  • Open platform to access more external applications
  • Split-screen mode to complete reading, writing and drawing on one screen


XNUMX. Features

1. AI generates a mind map in one sentence

Xiaobai who has never done a mind map does not know where to start?Don't panic!Put forward the demand, combined with the hottest ChatGPT at the moment, Treemap AI can directly help you generate a customized mind map with one click, and also supports random modification, and new users will get a 2000-word AI word count experience for registration!Free wool, don't you still grab it?

The AI ​​mind map intelligent library has complete and powerful functions. Whether you are reporting work or refining reading notes, even the most popular video script analysis, it can help you generate accurately, greatly improving your life and work efficiency!And the smart library will continue to be updated, so there will be no duplication or outdated content.The generated mind map is not limited to the addition and deletion of nodes, where it needs to be changed!

No inspiration for the mind map you made yourself?You can also let AI refer to your ideas to continue to expand, and refuse to run out of inspiration!Let you stand on the shoulders of giants to broaden your horizons, and let your thinking and inspiration emerge immediately.

2. Wide range of application scenarios

Whether it is demand sorting, product planning, market analysis, or team communication, as long as it involves sorting and transmitting information, TreeMind tree diagram can provide you with great help.

  • need to organize: Visually display the relationship between various requirements through the map, helping the team to have a clearer understanding of the direction of the product.
  • product planning: The product life cycle, functional modules, user experience and other key points are displayed at a glance, which is convenient for the team to refer to and implement.
  • Market analysis: Combined with AI technology, it intelligently extracts key information from market data, generates an intuitive mind map, and helps product managers quickly grasp market trends.
  • team communication: In meetings or daily work, use the TreeMind tree diagram to record and organize key points in real time to ensure effective communication. TreeMind tree diagram can even allow you to break away from PPT and directly use mind map for presentation.

3. Millions of templates are free

Nearly 150 million + map templates, new ones are added every day!Contains as many as 829 featured albums, a total of 123 template categories, covering 12 industry types, helping you unleash the unlimited potential of creativity!No matter what type of mind map you want, you can find 99% of it in the TreeMind tree diagram template library!Modify directly on the master's mind map to help you broaden your thinking and become a mind map master.

4. Split screen view, read and write on one screen

When drawing a complex mind map, it is common practice to refer to a large amount of information, but the traditional method of frequently jumping to the window to view information and edit the mind map is really inefficient.

One screen dual use makes your reading and writing mode so easy!While reading documents, while making mind maps, the learning effect is doubled!Data upload supports 3 import methods: file import, paste import, URL import, supports PDF, Word, Txt three file formats, no need to convert, direct reference!What's even better is that the historical database is automatically backed up, so you don't have to worry about file loss anymore!In addition, we have also added the window size adjustment function, adjust the interface as you like, and provide a more comfortable learning environment!The connection of the two makes your map drawing smoother!

5. Team space, multi-person collaboration

Whether you are working from home during a special period, or you are collaborating with multiple people to complete a project/assignment, you need to easily share your creative content with your colleagues. From time to time, you also need to have a multi-person brainstorming session, where multiple people work together to complete a task online .Traditional software can only complete one file for each person, and finally merge the files of multiple people together, and then modify them uniformly.

  • Traditional mind map:Map files or pictures can only be shared through WeChat, DingTalk, email, etc., and need to be shared again after the content is updated; it is impossible to complete the production of a mind map with classmates, colleagues, or multiple people at the same time;
  • A new generation of mind map-TreeMind tree map:Many people can edit a mind map at the same time, and can brainstorm and sort out ideas in a mind map.

6. Cloud cross-platform

Most of the mind-mapping software currently on the market needs to be downloaded and installed as a client before they can be used. They either take up computer disk space or waste mobile phone memory, and the most important thing is that they occasionally encounter pirated software and rogue software. TreeMind is an online mind mapping tool. Open a browser and visit the website to quickly create a mind map, let your inspiration quickly shuttle through the nodes of the mind map, and maximize your learning and productivity.

At the same time, the TreeMind tree map realizes "real-time saving, and the content can be synchronized with multi-platform files". You no longer have to worry about forgetting to save, software crashes and causing content loss, allowing yourself to focus on content creation and inspiration explosion without other interference ~ In the browser, Both the client and the mobile phone can modify and browse files.

7. Free is enough, members are super valuable

Most of the rights and interests of TreeMind tree map can be used for free. For beginners who are new to mind mapping, TreeMind tree map also provides the supreme experience of exporting once a day for free ➕ AI words of 1 words; Unlock more rights and interests and become the master of mind mapping!Colleagues buy together in a group and enjoy group discounts.

XNUMX. How to use TreeMind tree diagram?

1. Want AI one-click generation:

Open the website and enter your needs in the text box, and AI will automatically generate a relevant customized mind map.

For example, I enter here:

Let it help me generate a weekly work report!Just enter a subject and click Smart Generate.The result is shown in the figure below:

2. I am not satisfied with the AI ​​map and want to create it myself:

In the workbench, select the type of map you want to create, and you can create and draw your own map!

For example: if you want to customize your own weekly work report, you can freely draw on the nodes as long as you choose a suitable map frame, and you can also modify the existing templates in the template library for free.The result is shown in the figure below:

XNUMX. Limited Time Offer

Shocking good news! TreeMind tree diagram is also online for life members now,The top 20 fans who buy every day can enjoy an instant discount of 200 yuan, and the lifetime VIP only needs an early bird price of 399 yuan!Countdown to 5 days!Hurry up to buy!

After reading it, are you surprised by the excellent functions and considerate service of TreeMind? Product managers who want to improve work efficiency can try it. Its membership price can be said to be the price of cabbage.One member can be used on multiple platforms, which is very cost-effective.Strong Amway everyone to start!

Click and play without download :

Classification feature

Classification features are an important class of features.Classification features are discrete and non-continuous.

This article will introduce 5 mainstream coding methods for small and large classifications.And their respective advantages and disadvantages.


What are classification (category) characteristics?

Categorical features are used to represent classification. Unlike numerical features, which are continuous, categorical features are discrete.

such as:

  • 性别
  • city
  • Colour
  • IP address
  • User's account ID

Some classification features are also numerical values, such as account ID and IP address.But these values ​​are not continuous.

Continuous numbers are numerical features, and discrete numbers are categorical features.

For continuous and discrete explanations, you can read this article: "Understanding of continuous and discrete"

Encoding of small classification features

Natural Number Encoding/Sequence Encoding-Ordinal Encoding

Certain classifications have a certain order, in this case, simple natural number coding can be used.

For example degree:




One-Hot Encoding-One-Hot Encoding

For city, color, brand, material... these features are not suitable for coding with natural numbers, because these features have no ordering relationship.

The use of one-hot encoding can make different categories in an "equal position", and will not affect the classification because of the magnitude of the value.

For example, color classification (assuming there are only 3 colors):


Yellow -010


Similar to one-hot encoding, there are "Dummy Encoding" and "Effect Encoding".

The implementation is similar, but there are some slight differences, and it is applicable to different scenarios.

Those who are interested can read this article:

'The difference between dummy variables and one-hot encoding"

'Assignment method: effect coding"

Encoding of large-scale classification features

Target Encoding

Target encoding is a very effective method to represent classification columns, and it only occupies a feature space, also known as mean encoding.Each value in this column is replaced by the average target value for that category.This can more directly express the relationship between categorical variables and target variables.

Extended reading of the target code: "Introduction to Target Encoding"

Hash encoding

The hash function is also a hash function that everyone often hears.The hash function is a deterministic function that maps a potentially unbounded integer to a finite integer range [1, m].

If there is a category with 1 values, if one-hot encoding is used, the encoding will be very long.With the use of hash encoding, no matter how many different values ​​there are in the classification, it will be converted into a fixed-length encoding.


The thinking of bin counting is a bit complicated: instead of using the value of a categorical variable as a feature, he uses the conditional probability of the target variable taking this value.

In other words, we do not encode the value of the categorical variable, but calculate the correlation statistics between the value of the categorical variable and the target variable to be predicted.

Summary of the advantages and disadvantages of different encodings

One-Hot Encoding-One-Hot Encoding


  1. easy to accomplish
  2. The classification is very precise
  3. Can be used for online learning

Things to note:

  1. Inefficient calculation
  2. Unable to adapt to growthable categories
  3. Only applicable to linear models
  4. For large data sets, large-scale distributed optimization is required

Hash encoding


  1. easy to accomplish
  2. Model training costs are lower
  3. Easy to adapt to new categories
  4. Easy to handle rare types
  5. Can be used for online learning

Things to note:

  1. Only suitable for linear models or kernel methods
  2. Unexplainable features after hashing
  3. Accuracy is difficult to guarantee



  1. Minimal computational burden during training
  2. Can be used for tree-based models
  3. Easy to adapt to new categories
  4. Use back-off method or minimum count graph to handle rare classes
  5. Explainable

Things to note:

  1. Need historical data
  2. Need to delay update, not completely suitable for online learning
  3. Very likely to cause a data breach

The above content is taken from: "Proficient in feature engineering"

Final Thoughts

Categorical features are discrete features, and numerical features are continuous.

For small classifications, commonly used encoding methods are:

  1. Natural Number Encoding/Sequence Encoding-Ordinal Encoding
  2. One-Hot Encoding-One-Hot Encoding
  3. Dummy Encoding-Dummy Encoding
  4. Effect Encoding-Effect Encoding

For large classifications, commonly used coding methods are:

  1. Target Encoding
  2. Hash encoding
  3. Bin-Counting

Recommended articles:

'Machine learning category feature processing"

'Feature Engineering (XNUMX): Category Features"

Numerical features

Numerical features are the most common feature type, and numerical values ​​can be directly fed to the algorithm.
In order to improve the effect, we need to do some processing on numerical features. This article introduces 4 common processing methods: missing value processing, binarization, bucketing, and scaling.

What is a numerical feature?

Numerical features are features that can be actually measured.E.g:

  • Human height, weight, three-dimensional
  • The number of visits to the product, the number of times it was added to the shopping cart, and the final sales volume
  • How many new users and returning users among the logged-in users


The features of the numerical class can be directly fed to the algorithm, why do we need to deal with it?

Because good numerical features can not only show the information hidden in the data, but also consistent with the model's assumptions.A good effect can be improved through proper numerical transformation.

For example, linear regression and logistic regression are very sensitive to the size of the value, so it needs to be scaled.

For numerical features, we mainly focus on 2 points:

  1. 大小
  2. distributed

The four processing methods mentioned below are optimized around size and distribution.


4 common processing methods for numerical features

  1. Missing value processing
  2. Binarization
  3. Divide buckets/bins
  4. Zoom


Missing value processing

In actual problems, we often encounter data missing.Missing values ​​will have a greater impact on performance.So it needs to be dealt with according to the actual situation.

There are three commonly used processing methods for missing values:

  1. Fill in missing values ​​(mean, median, model prediction...)
  2. Delete rows with missing values
  3. Ignore it directly, and feed the missing value as part of the feature to the model for learning



This processing method is usually used in counting scenarios, such as: the number of visits, the number of times a song has been listened to...


Predict which songs are more popular based on the user’s listening music data.

Assuming that most people listen to songs very averagely and will listen to new songs continuously, but there is a user who plays the same song 24 hours a day, and this song is very partial, resulting in a particularly high total number of listening to this song .If the total number of listening times is used to feed the model, it will mislead the model.At this time, you need to use "binarization".

The same user has listened to the same song N times, and only counts 1, so that everyone can find songs that everyone likes to recommend.


Divide buckets/bins

Take the income of each person as an example. The income of most people is not high, and the income of a very small number of people is extremely high and the distribution is very uneven.Some have a monthly income of 3000, and some have a monthly income of 30, which is several orders of magnitude.

This feature is very unfriendly to the model.This situation can be handled by bucketing.Bucketing is to divide numerical features into different intervals, and treat each interval as a whole.

Common bucketing:

  1. age distribution
  2. Commodity price distribution
  3. Income distribution

Commonly used bucketing methods:

  1. 固定数值的分桶(例如年龄分布:0-12岁、13-17岁、18-24岁…)、
  2. Quantiles and buckets (for example, the price range recommended by Taobao: 30% of users choose the cheapest price range, 60% of users choose the medium price range, and 9% of users choose the most expensive price range)
  3. Use the model to find the best bucket



Linear regression and logistic regression are very sensitive to the magnitude of the value, and the large difference between different feature scales will seriously affect the effect.Therefore, the values ​​of different magnitudes need to be normalized.Scale different orders of magnitude into the same static range (for example: 0~1, -1~1).

Commonly used normalization methods:

  1. z-score normalization
  2. min-max standardization
  3. Row normalization
  4. Variance scaling

Extended reading:

'Data scaling: standardization and normalization"

'106-Data scaling (standardization, normalization) those things"

Exploratory Data Analysis | EDA

Exploratory data analysis is the process of obtaining the original data and using technical means to help oneself better understand the data, extract "good features", and establish a preliminary model.

This article will introduce how to classify data and how to visualize different types of data.

What is exploratory data analysis?

When it comes to basketball, everyone knows that height and wingspan are the key characteristics of athletes.

What about handball?I believe most people can't tell.

When you encounter a field you are not familiar with, you need to quickly have a certain understanding of the unfamiliar field.

There are 2 ways to help us understand unfamiliar areas:

  1. Consult industry insiders.Senior industry insiders will pass on some of their experience.
  2. Go and study data in unfamiliar areas.We can take the physical data and performance data of handball players for analysis to see what are the characteristics of the best handball players.Without any industry experience, some discoveries can be made through data insights.

The second way above is:Exploratory Data Analysis | Exploratory Data Analysis | EDA

Exploratory data analysis is a data analysis method and concept that uses various technical means (most of which are data visualization) to explore the internal structure and laws of data.

The purpose of exploratory data analysis is to gain as much insight as possible into the data set, discover the internal structure of the data, extract important features, detect outliers, test basic hypotheses, and establish preliminary models.

The 3-step approach to exploratory data analysis

The process of exploratory data analysis is roughly divided into 3 steps:

  1. Data Classification
  2. data visualization
  3. Insight data

The first step: data classification

When we get the data, the first step is to classify the data, and then use different methods to process different types of data.

The data can be classified in the following ways from coarse to fine:

Structured data vs unstructured data

Structured data: Data that can be organized in tables is considered structured data.

For example: data in Excel, data in MySQL...

Unstructured data: All are organized in non-tabular format.

For example: text, picture, video...


Quantitative data vs qualitative data

Quantitative data: Numerical type, which measures the quantity of something.

For example: 1985

Qualitative data: category, describing the nature of something.

For example: post-80s


4 levels of data

Norminal level: It is the first level of data, and its structure is the weakest.Just need to sort by name.

For example: blood type (A, B, AB, O), name, color

Ordinal level: Sequencing level adds natural sorting on the basis of categorization level, so that we can compare different data.

For example: the star rating of the restaurant, the evaluation level of the company

Interval level: The fixed distance level must be of numeric type, and these values ​​can be used not only for sorting, but also for addition and subtraction.

For example: Fahrenheit, Celsius (the temperature has a negative number, multiplication and division are not allowed)

Ratio level (ratio level): On the basis of the fixed distance level, the absolute zero point is added, which can not only perform addition and subtraction operations, but also multiplication and division operations.

For example: money, weight


Step XNUMX: Data visualization

In order to have a better insight into the data, we can visualize the data to better observe the characteristics of the data.

There are several commonly used data visualizations:

The four data levels above need to correspond to different visualization methods. Below is a table that can help you choose a better visualization solution.

The following are some basic visualization schemes. In practical applications, there will be more complex, combination charts can be used.

Data level attribute Descriptive statistics chart
Classify Discrete, disordered Frequency ratio, mode Bar chart, pie chart
Sequencing Ordered categories, comparison Frequency, mode, median, percentile Bar chart, pie chart
Fixed distance Number difference is meaningful Frequency, mode, median, mean, standard deviation Bar chart, pie chart, box plot
Fixed ratio continuous Mean, standard deviation Bar chart, curve chart, pie chart, box plot

Step XNUMX: Insight into the data

Data visualization can help us gain better insights into the data. We can more efficiently discover which data is more important, the possible relationships between different data, and which data will affect each other...

The reason why it is called exploratory data analysis is that there are no fixed routines, so there is nothing to talk about in this step.

Final Thoughts

Exploratory data analysis is a data analysis method and concept that uses various technical means (most of which are data visualization) to explore the internal structure and laws of data.

The process of exploratory data analysis is roughly divided into 3 steps:

  1. Data Classification
  2. data visualization
  3. Insight data