Quelles techniques pouvez-vous utiliser pour concevoir des fonctionnalités dans un projet ML ?

Data is never cleaned. One needs to differentiate between noise/errors vs outliers/missing values. The former may be genuine "dirty" while the latter may have hidden insights. Over cleaning may lose those valuable insights while under cleaning may not produce a reasonably accurate model. Investigate the true cause before dropping or imputing them.

Data cleaning is an important yet tricky part of the process. It's possible to clean data to such an extent that it no longer represents the original observation. Indeed, data must be cleaned as the initial part of the pipeline, but avoid forcing excessive cleaning just to make your model appear effective. Sometimes, the data simply won't support the creation of a good model. In such cases, it's important to accept this and draw appropriate conclusions.

In the real world, no data is clean. Neither is it too dirty. But data cleaning is the most crucial stage of your pipeline. A bad data will give false insights and will fail the ML model. To clean any data, you should know certain attributes of the data itself like: 1. What domain does the data belong to and what are its constraints? 2. How trustable is the data collection process? 3. What fields in data are more significant for the analysis/ML model? 4. How much percent of the data is missing? Answering the above questions will help you in these ways: 1. will reveal your outliers and erroneous data 2. will reveal the noise in your data 3. will give you the priority for data cleaning 4. will help you decide whether to impute or drop.

In the realm of feature engineering for ML projects, effective data cleaning entails addressing missing values through imputation, dealing with outliers through trimming, transforming numerical features, encoding categorical variables using methods such as one-hot or label encoding while addressing rare categories, cleaning text data through techniques such as removing stop words, stemming, or lemmatization, converting text to numerical features such as TF-IDF, and ensuring consistent sc It is critical for improving and enhancing the ML model to evaluate the impact of these strategies on a regular basis using cross-validation and measuring feature importance.

In the realm of data science, envision data cleaning as the meticulous art transforming raw information into a foundation for analytical brilliance. Like a craftsman refining a rare gem, eliminate flaws with precision. Scrutinize missing values and banish duplicates, ensuring a harmonious blend of variables. Navigate outliers like a masterful storyteller, acknowledging their impact without allowing them to dominate. Data cleaning is where raw data metamorphoses into the gold standard for insightful analyses. Aspire to be the virtuoso, and your data will resonate with the symphony of accurate, reliable, and transformative insights.

Be aware that data transformations need to be tailored to each ML model, because they have different capabilities handling the data. For instance, it is recommended to normalize the data when using neural networks while for a tree-based model it may not be necessary. Similarly, tree-based methods may handle categorical variables and missing values while other models can't. Data transformations can also depend on the ML task. For example, for a given task, it could be better to reduce the number of categories of high cardinality variables and then to apply one hot encoding. The main takeaway is: data transformations depend on the task and the ML model to be used

- Machines talk binary (0 and 1). - Programs talk programming languages (python, C...) - ML models USUALLY work with numerical values. To transform your features into numerical values you can use: - Standard scaler - Change column type - Map values directly to features PS: This applies to text-based ML models.

Imputing missing values, trimming outliers, transforming numerical features, standardizing or normalizing scales, encoding categorical variables, cleaning and converting text data, extracting information from datetime features, assigning values to ordinal variables, creating features for interactions, and handling duplicates are all key techniques in data transformation for machine learning. Regular examination of these modifications is required to optimize the performance of machine learning models.

There is so much to say here, but I would mention little useful tips that I followed: - If you have a linear algorithm, and you transform categoric variables with WOE or other this type of weighted transformation methods, try to provide a linear relationship between the independent and target variables. Use the optimal inning method and look out not to overload one of the bins which leads to high concentration; - Before scaling, always consider and analyze the outliers; - If you have high cardinality for a variable, try to group it. Follow a reasonable and explainable business logic; - Sometimes other variables may be helpful. For example, if you have a "job title" and salary variable, you can replace each job with its average salary.

In your pursuit of mastery in data science, recognize data transformation as the alchemical process shaping raw data into a potent elixir of insights. This practice distinguishes novices from experts, an art form refining data to reveal concealed patterns. Through normalization, standardization, and feature engineering, transform your dataset into a harmonious symphony. Embrace data transformation strategically, ensuring your analytical endeavors transcend the ordinary, paving the way for a transformative impact in the world of data.

Besides analyzing variable importance and using dimensionality reduction methods, it is good to follow t-statistics, residuals, intercept, and ad R-squared. It seems so basic, but it explains much. - Extremely high intercept may be a sign of bad feature selection; - Try adding new features and extracting non-logical ones and analyze how adj R-squared changes; - Test residuals normality, residual autocorrelation, and heteroscedasticity. These test signals about using unnecessary variables and not considering impactful ones losing them inside residuals. Residuals should normally distributed and shouldn't correlate with the target variable, as it represents random differences. If it is not, then maybe we have forgotten something.

Addressing missing values through imputation methods such as mean, median, or mode; handling outliers via trimming; applying dimensionality reduction techniques such as principal component analysis (PCA) to transform and capture key information from numerical features; encoding categorical variables efficiently through methods such as one-hot or label encoding; extracting relevant information fro It is critical to evaluate the influence of various data reduction approaches on machine learning model efficiency and performance on a regular basis.

In the realm of data science, view data reduction as the art of distillation, extracting essential insights from the vast sea of information. Think of it as simplifying datasets strategically, eliminating noise and highlighting crucial patterns. Aspiring data scientists must discern the signal from the noise, shaping analyses with finesse to reveal the core narrative. Mastering data reduction ensures your insights resonate with precision and depth in the world of data science.

Remember to take correlation between features into account. When there's a lot of collinearity in the data, using a clustering algorithm to identify groups of highly correlated features is helpful. Calculate a pairwise distance matrix and find clusters in that, then select the strongest feature in each cluster using filter or wrapper methods. Dimensionality reduction techniques can be very powerful, but they should be used with care. Make sure you treat them the same as you would other ML algorithms when partitioning your data - only ever fit them on in-sample observations. And they are best used when interpretability of the resulting models is only a minor consideration.

Imputing missing values using mean, median, or mode; addressing outliers through trimming; generating new features by transforming numerical data or creating interactions; encoding categorical variables efficiently using techniques such as one-hot or label encoding; and extracting relevant information from datetime features for meaningful representation are all critical techniques in the context of data generation for machine learning. Furthermore, for text data, strategies such as producing numerical representations such as TF-IDF or word embeddings help to increase feature richness. It is critical to evaluate the effects of various data production approaches on a regular basis in order to improve the performance of ML models.

Data generation is crucial for pushing the boundaries of AI. It enriches datasets, unlocking hidden insights and boosting model performance. By creating synthetic data and crafting meaningful features, we can uncover deeper levels of understanding and train more robust models. This is especially valuable for startups, where speed and agility are key. However, careful planning and execution are necessary to ensure generated data is relevant, accurate, and unbiased. As we move forward, data generation will fuel innovation and shape the future of intelligent machines.

Data encoding is at par to ML algorithm selected and is critical in increasing the power of the model. Usually, it handles issues over the training dataset e.g. imbalanced datasets, converting continues vars to bined categorical vars and many more. From my experience, data encoding should also test and handle bias and correlations in the training data. There are many statistical and algorithm based methods to help on this process bootstraping, unsupervised clustering, econometric models to reveal seasonality, cyclicality in the data etc. We should enhace our understanding of the data and the problem to be solved before applying ML models, and data enconding is more like an art, rather than an exact science process.

One-hot encoding is one of the more straightforward data encoding methods. It's used on nominal features to prevent a learning algorithm from picking up an order in the data by creating a new column for each categorical value in that column which evaluates to true or false, i.e. if a column in a dataset is named “colour” and contains three categorical values “red”, “green”, “blue” one-hot encoding such a feature would return three new columns: colour_red, colour_green and colour_blue and their rows evaluate to either 0 or 1.

I have found that using the feature store in Vertex AI helps regularize the feature engineering process and leverage work across projects and workflows by acting as a repository of data that has been previously validated

In AI research, validating data post feature engineering is crucial. Use methods like profiling, visualization, or testing. Tools like pandas-profiling or sweetviz offer detailed reports. Matplotlib or seaborn aid in exploring data nuances. Employ pytest or unittest for robust unit tests. This holistic approach underscores the pivotal role of ongoing assessment in refining ML features, a key facet in ensuring optimal model performance.

Business, operations and industry acumen tailored to the problem at hand always adds to feature selection. Ask your partners to give you insights on their way to do business and test these assumptions against available data. It will save you time and also will allow your partners to be involved and better understand the process. A feature store might help on this direction.

I nearly always start with hand-crafted features as a first step. It helps with future feature engineering because it forces me to think about WHY certain features are likely to drive value. And this can often lead to (1) the right feature generation strategies, and (2) requesting more data to be logged by whatever application or process is generating the data.