Comment générer des prédictions, des recommandations ou des classifications avec l’apprentissage automatique ?

Define Problem: Clarify the problem and goals. Collect Sources: Identify data sources like databases, APIs. Gather Data: Obtain data from chosen sources. Clean Data: Handle errors, missing values, outliers. Integrate Data: Combine data from various sources if needed. Feature Engineering: Select/create relevant features. Transform Data: Normalize, scale for consistent scales. Split Data: Divide into train, validation, test subsets. Address Imbalance: Handle imbalanced classes if applicable. Visualize Data: Explore data patterns visually. Manage Bias: Be aware of and address biases. Store Data: Organize data for easy access

In my experience, preparing data for machine learning is a key step that greatly impacts how well your model will work. First, you clean the data by filling in missing or wrong information. Next, you adjust the data to make it easier for the model to understand. This can include turning categories into numbers or scaling down large numbers. Then you divide your data into groups for training, checking, and testing your model. It's also important to make sure you're not accidentally using test data during training, as this can give you misleading results. Overall, careful data preparation is crucial for creating a reliable model.

When working with signals from sensors, a crucial step often overshadowed by domain-specific knowledge is feature extraction. To establish a clear baseline recently, I've turned to use tsfel in Python for this purpose. Alongside, I've employed several feature selection algorithms available in scikit-learn. For unsupervised models, I've considered removing features with low variance. And when labels are available, I've used recursive feature elimination. Having this clear baseline and a set of pertinent features in hand, we can then delve into crafting our own features, leveraging domain-specific expertise.

Most of the work in a DS project is in pre-processing. It's not the glamorous stage but essential to your entire project. It is important that you can trust the data that your model is built upon. In my experience, common questions to keep in mind as you prepare the data in the right format are: (1) Missing data: How do you deal with missing data? Missing at Random? What type of imputation would be suitable? (2) Unbalanced data: random splitting to training and validation split won't work well if your data is imbalanced (eg. for detecting rare events). ML model will end up training for majority if you don't take care. (3) How are the input features distributed? Need scaling or normalization? (4) Correlation between input features.

"Purposeful" data collection is the essence of machine learning. Having lots of predictors in hand does not necessarily lead to a machine learning model with high predictive accuracy. Irrelevant or extraneous data can introduce noise and make it harder for the model to discern meaningful patterns. Also, data collection can be very expensive in terms of time, manpower, and resources. Therefore, it is important to identify the predictors that are (1) relevant to the prediction task, (2) less prone to issues such as missing values or outliers, and (3) unbiased. With purposeful data collection, one can set the stage for building more accurate, fair, and efficient machine learning models.

My humble suggestions: - start with a simple model and then make it more complex if necessary - use regularization techniques to prevent overfitting - make cross-validation or bootstrapping to make the model robust - consider scalability and optimization from the beginning

Choose Model: Select appropriate algorithm based on problem type and data characteristics. Hyperparameters: Set initial values for model parameters. Train Model: Feed training data into the model. Loss Function: Define a loss function to measure model's performance. Optimization: Algorithm adjusts parameters to minimize loss. Validation Set: Tune hyperparameters using validation data. Regularization: Apply techniques like L1/L2 to prevent overfitting. Evaluation Metrics: Choose metrics (accuracy, F1-score) to assess model performance. Iterate: Adjust model, hyperparameters, and features based on results. Cross-Validation: Validate model on different data splits for robustness. Finalize Model: When satisfied, test model on unseen test data.

Selecting and training a machine learning model is more nuanced than it appears. First, consider operational aspects along with performance. A complex model may offer high accuracy but could be resource-intensive and hard to explain. Second, be mindful of the bias-variance tradeoff. Striking a balance between overfitting and underfitting is essential for a model's success on new data. Third, a model is never really 'done.' Post-launch, it needs regular updates based on real-world feedback and changing conditions. Overall, the process is dynamic and multifaceted, requiring a thoughtful approach to immediate metrics and long-term adaptability.

Traditional machine learning models have their merits, but newer, more complex models like neural networks can bring out hidden insights. The decision to select a particular model should not only consider statistical performance metrics but also operational concerns like interpretability, scalability, and computational costs. When working with clients, I've found that simple models often win out when stakeholders need to understand and trust the model's decisions.

Depends on your problem but for structured datasets everything can be achieved with trees, while unstructured is best tackled by neural nets. Training-validation-test splits must be done according to the problem you are working with, if time series, you must make sure the splits are walking forward.

Evaluation Metrics: Assess model using appropriate metrics (accuracy, precision, recall). Test Data: Use unseen test set to evaluate generalization. Performance Analysis: Identify strengths, weaknesses, areas of improvement. Fine-tuning: Adjust model based on evaluation results if needed. Deployment Strategy: Choose deployment approach (cloud, on-premises). Scalability: Ensure model can handle real-world load. Monitoring: Implement continuous monitoring for model health. Feedback Loop: Collect user feedback to refine model over time. Ethical Considerations: Address potential biases, privacy concerns. Version Control: Keep track of model versions for reproducibility. Documentation: Create clear documentation for maintenance.

From this perspective, the final steps of a machine learning project are as crucial as the initial ones. Evaluating your model involves more than just running it on a test set. Choosing the right metrics, such as accuracy or F1 score, helps you better understand its strengths and weaknesses. Error analysis, breaking down mistakes into types like false positives and false negatives, can offer insights for improvement. Once deployed, continuous monitoring is key. Whether your model lives in a web app, mobile app, or cloud service, you must watch for changes in data or performance and update accordingly. Setting up a user feedback loop can also provide valuable real-world adjustments, as well as version control.

When deploying a machine learning (ML) model, there are key steps to consider. These include selecting the right framework, optimizing the model for the target environment, converting the model into a compatible format, integrating it into the application or service, testing it for performance and accuracy, setting up monitoring and maintenance systems, validating its performance, ensuring scalability and availability, prioritizing security, and using model lifecycle management platforms. The best deployment strategy will depend on your specific model, data, and use case. Experimentation is key to finding the optimal approach.

Use cases from my area (HR Analytics): * attrition analysis * employee rating/appraisal analysis Classification used in both cases to assess variable/feature importance. I would emphasize the importance of planning and preparation: - what are we setting out to do, - what data do we have, - what additional data can we get or compute, - of this what do we want to use and - what can we use (ethical considerations). I am not advocating for waterfall analytics as opposed to CRISP-DM, but in my experience careful planning before I even open the console can eliminate a lot of _unnecessary_ loops of "back to square one" coding.

A model's business impact is just as crucial as its statistical metrics. Often overlooked is the change management aspect of deploying a model, especially in traditional industries. From my consulting experience, even the best models can fail if they are not embraced by the workforce or if they disrupt established workflows without adequate preparation.

Stock Market: Predict stock price trends using historical data and sentiment analysis. Healthcare: Forecast disease outbreaks based on epidemiological data and climate factors. E-Commerce: Recommend products to users based on browsing and purchase history. Energy Consumption: Predict energy usage patterns to optimize resource allocation. Customer Churn: Predict customer churn likelihood by analyzing behavior and interactions. Credit Scoring: Assess creditworthiness of applicants using financial and personal data. Traffic Congestion: Forecast traffic congestion patterns for urban planning and optimization. Crop Yield: Predict crop yields with data on weather, soil, and agricultural practices

Biomedical signal: Predictions of patients with sleep apnea from oxygen saturation data. Clinical scenario: Determine high risk cases of abdominal aortic aneurysm based on thickness and curvature parameters. Farm animal example: Utilization of external anatomical features from farm animals to predict animal weight.

In my experience, data quality is a cornerstone of effective predictive modeling in machine learning. An incomplete or inconsistent dataset can seriously compromise the model’s predictive accuracy. Additionally, the relevance of features must be carefully considered to improve both model performance and interpretability. Balancing the dataset, particularly in classification tasks, is crucial to avoid biasing the model towards an overrepresented class. Furthermore, normalizing or standardizing the data can optimize model performance, and maintaining data integrity is especially vital in sectors like healthcare or finance where inaccurate predictions could lead to severe consequences.

Regression --> House price prediction - To get price of house using different parameters - Loan Prediction Credit Rating - Ratings given to the companies as the performance parameters Classification --> Fraud/Anti Money Laundering classification - To detect customers/cases who are potential fraudsters/money launderers Loan Default Prediction - whether the loan should be given or not to the customer FD subscription classification - customer will subscribe to term deposit or not to marketing of banking Time Series Forecasting --> - Stock Market closing price - Weather Forecasting

Machine learning (ML) models are powerful tools that can help in various areas such as budget planning, decision-making, and resource optimization. They analyze data, predict trends, and provide insights to improve financial forecasting, automate decision-making, and optimize resource allocation. With ML, organizations can make informed choices, enhance performance, and achieve better financial and operational outcomes.

Movie Streaming: Suggest movies based on user preferences and viewing history. Music Platforms: Recommend songs and playlists using listening habits and genre preferences. E-Commerce: Provide personalized product recommendations to enhance shopping experiences. News Aggregators: Offer news articles aligned with user interests and reading history. Travel Planning: Recommend destinations, accommodations, and activities based on user preferences. Book Selection: Suggest books based on reading history and genre preferences. Social Media: Show relevant connections, posts, and content to users on social platforms. Food Delivery: Recommend restaurants and dishes tailored to user tastes Fitness Apps: Provide workout routines and nutrition plans s.

In addition to customer-facing recommendation engines, machine learning can also assist in internal decision-making processes. For example, by analyzing operational data in real-time, machine learning can recommend preventative maintenance activities, thereby extending the lifecycle of machinery and reducing operational downtime.

Recommendation systems in machine learning (ML) boost sales, engagement, and customer satisfaction with personalized suggestions. They increase conversion rates, enhance user engagement, and improve the overall shopping experience. These systems are essential for businesses to remain competitive in the digital marketplace.

Recommender systems helps users find items related to their interests easily in various business scenario: entertainment (Netflix/Spotify), e-commerce (Amazon), social network (LinkedIn)... One of the key challenges in the recommendation is the balance between the diversity/novelty and the accuracy. Solution: we shall define users' current main intent before choosing the relevant recommendation algorithm.

Recommendation systems in machine learning (ML) boost sales, engagement, and customer satisfaction with personalized suggestions. They increase conversion rates, enhance user engagement, and improve the overall shopping experience. These systems are essential for businesses to remain competitive in the digital marketplace.

Classification in machine learning is a supervised learning technique used to predict the category or class of data. It helps filter and organize data, identify patterns, and has various applications like spam detection, medical diagnosis, credit approval, and face detection. Classification plays a crucial role in improving efficiency and decision-making in different domains, benefiting businesses and organizations.

Some common biomedical examples include: - Classification of patients based on phenotype - Clustering of patients based on multitude of quantified measures - Separation of blood vessels based on flow intensity

Machine learning can be used to classify new data points into predefined categories according to their features or traits. An algorithm could, for instance, be trained to categorize images of animals into various species based on their visual characteristics.

Classification tasks should not only focus on accuracy but also fairness and ethical considerations. For instance, while working on credit scoring models, it's crucial to ensure that the model does not inadvertently discriminate against any particular demographic group.

One of the applications of clustering can also be applied on marketing research. After collecting data of the lateral variables (like personality traits ) through surveys or interviews and then linking it to demographic details , clustering can be used to identify different groups within the sample and accordingly can be used to identify target segments

The real power of machine learning lies in its integration with business strategy. Businesses shouldn't deploy machine learning models just for the sake of innovation. Every project should start with a clear problem definition tied to business objectives, and its success should be measured against those objectives. This holistic approach ensures that machine learning serves as a tool to leverage better business decisions, rather than just a technological feat. By combining machine learning with business acumen and a focus on actionable insights, companies can unlock transformative value. My work has proven that this integration is not only possible but highly effective in capturing hidden opportunities and addressing complex challenges.

In machine learning, the human element can be as crucial as algorithms. A diverse team brings varied perspectives that can enrich model selection and fine-tuning. Moreover, metrics like accuracy aren't the only markers of success. In certain fields like healthcare, a model's ability to clearly explain its decisions may outweigh a slight edge in performance. Also, real-world effectiveness becomes clear only after deployment, emphasizing the need for ongoing monitoring and updates. These factors don't always fit neatly into formal procedures but can dramatically affect a project's outcome. Therefore, it's essential to consider these intangibles along with the hard numbers and algorithms.

Machine learning - just like the whole artificial intelligence - cannot be dehumanized. It is man who determines how algorithms work, it is man who writes a computer code, in effect it is man who controls machine learning and determines how it is implemented.

Machine learning models have revolutionized weather forecasting by analyzing historical data and training on various sources like weather station data and satellite imagery. These models provide detailed and accurate predictions for shorter timeframes, surpassing traditional methods. They can forecast temperature, thunderstorm intensity, and even climate risks. ML models are increasingly reliable and offer promising benefits for sectors like agriculture, transportation, and emergency response planning.

In my experience, the right sequence is to perform two stage search; first is model selection and then once you got the best model, use that model to search for the best preprocessing pipeline. Unless your model is neural network, the default parameters are good enough and no need to optimize the parameters of most models. Their default values are oftentimes good enough. In summary, use any preprocessing pipeline and search the best model then use that model to search for the best preprocessing pipeline.