Preparing the data -Building the classifier 

Building a classifier involves several steps, including data preparation, model selection, training, and evaluation. Here's a simplified outline of how you might approach building a classifier for determining whether photos are "interesting":

1. Data Collection: Gather a dataset of photos from Flickr. You'll need a labeled dataset where each photo is labeled as "interesting" or "not interesting." You can manually label photos yourself or use pre-existing datasets if available.

2. Data Preprocessing: Preprocess the images to prepare them for training. This may include resizing images to a standard size, normalizing pixel values, and augmenting the dataset with techniques like rotation, flipping, or cropping to increase variability.

3. Feature Extraction: Extract features from the images that will be used as input to the classifier. This could involve techniques like using pre-trained convolutional neural networks (CNNs) such as VGG, ResNet, or Inception to extract features, or manually designing features based on color histograms, texture descriptors, etc.

4. Model Selection: Choose a classification model architecture. Common choices for image classification include CNNs, which are well-suited for tasks involving image data due to their ability to capture spatial hierarchies of features.

5. Model Training: Split your dataset into training and validation sets. Train the chosen model on the training data and validate its performance on the validation set. Fine-tune hyperparameters as necessary to improve performance.

6. Model Evaluation: Evaluate the trained model on a separate test dataset to assess its performance. Common evaluation metrics for classification tasks include accuracy, precision, recall, F1-score, and ROC curves.

7. Model Deployment: Once you're satisfied with the performance of your classifier, you can deploy it to make predictions on new, unseen data. This could involve integrating it into a web application, mobile app, or other software systems.

8. Iterative Improvement: Continuously monitor and evaluate the performance of your classifier. You may need to retrain the model with additional data or fine-tune its parameters to maintain or improve performance over time.

Keep in mind that building an effective classifier requires careful attention to each of these steps, as well as experimentation and iteration to achieve the best results. Additionally, the specific implementation details will depend on factors such as the size and characteristics of your dataset, the computational resources available, and your specific use case requirements.