ML Interview Q Series: Why might the identical machine learning method yield varying levels of success even if the same dataset is used?

📚 Browse the full ML Interview series here.

Comprehensive Explanation

When an interviewer asks why a single algorithm can produce different success rates while training on the same dataset, a strong approach is to first identify the sources of stochasticity or inconsistency in machine learning workflows. The essential factors may include random initialization of model parameters, stochastic sampling or shuffling, variations in hardware execution order (especially on GPUs), distinct random seeds for each run, and differences in hyperparameters or train-validation data splits. Small differences in any of these factors can lead to substantial divergences in performance metrics, especially for complex or large-scale models.

Random initialization of parameters is particularly significant in neural networks, since the initial weights can influence how effectively gradient descent algorithms find different local minima or saddle points in the loss surface. The loss function for a neural network is typically high-dimensional, and many local minima may yield different levels of generalization on unseen data. Stochastic gradient descent further compounds the randomness because the mini-batch sampling can lead to different parameter updates from run to run.

One can understand these changes in model parameters by looking at the usual gradient descent update rule in a neural network:

Here, theta_t represents the model parameters (for example, weights in a deep neural network) at training iteration t, eta is the learning rate (a hyperparameter that controls the size of the step), and nabla_theta L(theta_t) is the gradient of the loss function with respect to the parameters at iteration t. Because mini-batch sampling is random, each run might see a slightly different sequence of parameter updates, thereby sometimes getting stuck in different local minima.

If different data splits are used, the model might train on slightly different subsets or even apply different cross-validation partitions, which can lead to small (or sometimes large) discrepancies in the performance metrics. Over time, these small differences can accumulate into noticeable shifts in final accuracy, precision, or recall.

Another subtlety arises from certain parallelization strategies on GPUs or multi-core CPUs where the order of floating-point operations can vary nondeterministically. Floating-point arithmetic is not strictly associative, so the order of operations can cause slight numerical differences that eventually become amplified in deep models.

Controlling the random seed is often the easiest way to ensure reproducibility, as it fixes the pseudorandom sequence for aspects like parameter initialization, mini-batch sampling, or data augmentation. However, some libraries and computational frameworks might still have nondeterministic behaviors due to low-level parallelization. Manually disabling such features (like cuDNN deterministic settings) can help, but performance might slow down. This tension between reproducibility and speed is an ongoing challenge.

Even subtle changes in hyperparameters—like the learning rate, momentum, dropout probability, or weight decay—can alter the trajectory of training in non-obvious ways, so ensuring that all hyperparameters remain fixed across runs is critical for consistent performance.

When interviewers probe on this topic, they may want to see if the candidate recognizes the distinction between the deterministic aspects of an algorithm and its actual implementation details. Demonstrating familiarity with controlling seeds, employing repeated experiments, and systematically validating model robustness across different initialization seeds shows strong command of reproducibility best practices.

Potential Follow-up Questions

Could random seeds entirely solve reproducibility problems?

Random seeds address part of the problem by ensuring that parameter initialization, data shuffling, and other random procedures start from the same state. However, in parallel or GPU settings, there might still be nondeterministic operations driven by underlying libraries. Some frameworks have operations that do not guarantee bitwise reproducibility. Consequently, setting a seed is a crucial first step, but additional measures like using deterministic kernels in cuDNN (when possible) are sometimes necessary to achieve perfectly consistent runs. In addition, external system factors, such as CPU multithreading or GPU concurrency, can still introduce slight fluctuations in floating-point arithmetic.

How do variations in hyperparameters affect the final performance?

Changing hyperparameters such as learning rate or regularization can drastically affect how quickly the model converges and which area of the loss landscape it ends up in. If the learning rate is too large, training might diverge or oscillate around poor minima. If it is too small, the model might converge slowly and get stuck in suboptimal local minima. Different regularization methods like dropout, weight decay, or batch normalization can also introduce randomness and varying degrees of parameter shrinkage. Even apparently minor alterations to hyperparameters (like the initial scale of weights) can lead to different performance outcomes because of the nonlinear nature of neural networks.

Why do floating-point precision issues matter in repeated runs?

Floating-point arithmetic is not perfectly associative or distributive due to the limited precision of floating-point representations. On single-threaded CPUs, the sequence of calculations might remain the same from run to run, resulting in minimal variation. On GPUs or multi-threaded CPUs, the non-deterministic order of floating-point operations can shift the accumulation of rounding errors. Although each difference is minuscule, these errors can cascade across many iterations of gradient updates. In large-scale or sensitive architectures, these tiny deviations can produce significantly different final parameter values over many epochs.

Can data augmentation strategies cause inconsistency in model performance?

Yes. Data augmentation is typically a stochastic process where images may be randomly cropped, rotated, flipped, or otherwise transformed. Even textual data augmentation can involve synonym replacement or random token masking. If the augmentation procedure is not carefully controlled by a fixed seed, each epoch may present substantially different augmented examples to the model. This can improve generalization but also introduce variability in training outcomes. If the goal is reproducibility, the candidate should ensure that the random transformations are seeded the same way in each run. However, in practical scenarios, it is acceptable to allow natural variability from data augmentation as long as the average performance is robust and stable.

How do we control for these different sources of randomness in practice?

One immediate technique is to set the seed for all random number generators used throughout the pipeline. In Python, this often involves controlling random.seed, numpy.random.seed, and the relevant seed controls for frameworks like PyTorch or TensorFlow. Below is an example in PyTorch:

import random
import numpy as np
import torch

def set_seed(seed=42):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed_all(seed)
    # For complete reproducibility, we might also want:
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False

Though this helps, it does not guarantee perfect reproducibility in every scenario, especially when GPU parallelism is involved. If a data pipeline or training library spawns multiple worker threads that each generate random augmentations, one must also synchronize seeds for those threads. Additionally, ensuring the exact same hardware environment and software versions can help mitigate subtle differences in floating-point arithmetic.

Ensuring the train-validation-test splits are exactly the same across runs or properly stratified can eliminate the variability from data splitting. Monitoring the environment, library versions, and even CPU instruction sets is important in high-stakes or research-critical scenarios where perfect reproducibility is necessary.

In summary, variations in success rates from the same algorithm and dataset generally stem from random initialization, nondeterministic operations, differences in data splits, or drifting hyperparameters. An experienced candidate would highlight random seed management, thorough experimentation protocols, and best practices for controlling or analyzing these variations, signaling strong mastery of the subject.

Below are additional follow-up questions

How can distributed or parallelized training setups lead to inconsistencies in model performance?

Distributed training involves multiple nodes or workers that asynchronously update shared model parameters. Each worker typically operates on a subset of the data, and updates get aggregated (e.g., through parameter servers or collective communication strategies such as AllReduce). The timing of these updates can be non-deterministic due to network latencies, varying hardware speeds, or load balancing. Since floating-point addition is not strictly associative, a different order of aggregation might lead to slight numerical discrepancies in the gradients or accumulated weights. Over the course of training, these tiny differences can accumulate and cause the final parameter values to diverge from a single-worker run. Furthermore, certain distributed optimizers schedule parameter synchronization or gradient averaging differently depending on hardware or process scheduling, which can also alter the convergence path of the model.

When using frameworks like PyTorch’s distributed package or TensorFlow’s multi-worker setup, ensuring reproducibility can be more difficult. Even if a random seed is set consistently in each worker, asynchronous parameter updates may occur in different sequences. If perfect reproducibility is required, the team might resort to synchronous training, carefully controlling the order in which updates are applied. However, this can reduce training speed and sometimes negate the benefits of parallelization.

Could the version of software libraries or hardware drivers affect model success rates?

Software frameworks (e.g., PyTorch, TensorFlow) and hardware drivers (e.g., CUDA, cuDNN versions) often have subtle performance and numerical handling differences across releases. A new version might change the default convolution algorithm, rounding modes, or internal caching strategies. Even seemingly small updates—like how a library implements fused kernel operations—can lead to minor numeric deviations. Over enough iterations, these small deviations can produce noticeably different results.

In extreme cases, a model that was stable under one version might diverge under a new version if the floating-point precision or parallelization approach changed. For example, older versions of cuDNN might have used a different deterministic kernel. A thorough approach to reproducibility includes documenting all library versions, enabling any deterministic flags available, and replicating the exact environment for each run. Continuous Integration setups often encapsulate these dependencies in container images or environment specification files to ensure consistent runs across multiple machines.

Is there a risk that minor differences in data preprocessing pipelines can alter outcomes?

Seemingly trivial changes in data preprocessing can have a cascading effect on final performance. For instance, resizing an image with a slightly different interpolation method (bicubic vs. bilinear) can subtly shift pixel values. Converting text from uppercase to lowercase in one pipeline but not another might impact token frequencies in NLP tasks. Handling out-of-vocabulary tokens, missing values, or outliers differently can also shift training data distributions enough to produce inconsistent model performance.

In many real-world data pipelines, transformations may be run in distributed or multi-threaded modes. The order in which transformations are applied, or the chunking of data, can shift sample boundaries or alter the data distribution in mini-batches. These details are often buried in code or depend on framework defaults. It is good practice to document the data preprocessing steps meticulously—explicitly listing interpolation methods, scaling factors, or any partial data cleaning procedure—to avoid unexpected variability.

In scenarios where we reuse or fine-tune pretrained models, how might that contribute to different success rates?

Pretrained models are often distributed as checkpoint files created on specific frameworks, hardware, or library versions. While the structure and weights might be the same, the fine-tuning process can introduce new randomness. Randomness may come from data augmentation, mini-batch ordering, or newly initialized layers added on top of the pretrained backbone. Even a single randomly initialized linear layer can alter the gradient path substantially during backpropagation.

Another subtle point is that certain pretrained checkpoints may have been saved in mixed-precision or use half-precision floating-point in some parts of the model. When loading these weights, discrepancies can appear if the new training environment interprets or upcasts them differently. If a pretrained checkpoint is intended for a different but similar architecture (e.g., BERT vs. a BERT-like model with slight differences in layer normalization implementation), loading those weights might require additional transformations that introduce minor numerical deviations. All these minor details can lead to variations in downstream performance when using the “same” model and dataset.

What are some edge cases that can happen if the dataset is relatively small or imbalanced?

When the dataset is small or highly imbalanced, variability in train/validation/test splits can have a larger impact on performance metrics. A single data point or outlier can skew performance if it ends up in the training set in one run and the test set in another. With imbalanced data, random splitting might yield very different class distributions among folds or random seeds, disproportionately affecting training. Class-weighting mechanisms or oversampling/undersampling strategies can vary from run to run if not strictly controlled by a fixed random seed, causing more pronounced fluctuations in model performance.

In small-data regimes, the model might overfit easily, and random initialization or subtle hyperparameter differences can drastically change the trajectory of training. A tiny change in a regularization hyperparameter or an early stopping checkpoint can determine whether the model memorizes a subset of the data or finds a more generalizable pattern. It is essential in these situations to employ consistent cross-validation splits, thorough hyperparameter searches, and multiple random restarts to gain a reliable estimate of model performance.

How do online learning or streaming data scenarios introduce further unpredictability?

Online learning or streaming data implies that data arrives in sequential chunks, and the model is updated as new samples come in. The sequence in which the data arrives can be random, or might change if the data pipeline’s ordering is not strictly enforced. If certain classes or patterns occur earlier or later in the stream, the model’s parameters might adapt differently. Over multiple runs with different random seeds, the ordering or segmentation of streaming batches might vary, producing distinct parameter updates and final metrics.

In practice, implementing online or streaming pipelines often involves specialized data structures, concurrency, and caching mechanisms that can further complicate reproducibility. For example, if a caching layer decides to evict certain data samples at random intervals, some runs might see slightly different samples at different times. If the streaming system is set to randomly sample data from a queue to feed the model, each run can produce a different training trajectory. Documenting these mechanisms and controlling the seeding at each stage is vital for debugging unexpected fluctuations in performance.

Could differences in early-stopping criteria or checkpoint saving policies cause variability?

Yes. Early stopping is typically triggered when the validation performance plateaus or begins to degrade for a specified number of epochs or iterations. If the validation metric fluctuates randomly from epoch to epoch, a slight fluctuation might trigger early stopping earlier in one run than in another. This difference in stopping point can lead to different final models, potentially affecting test performance. Similarly, some pipelines periodically save checkpoints based on a validation metric threshold. A small difference in the validation set or the random mini-batch order might cause one run to surpass that threshold earlier, thus saving and later reloading a slightly different checkpoint.

In extreme cases, if a model is on the cusp of a local minimum, slight changes in the mini-batch order or floating-point sums might allow it to escape or cause it to linger. This difference can be magnified by the early-stopping logic. Even slight changes in hyperparameters such as patience (the number of epochs to wait before stopping) or the frequency of checkpointing can cause the algorithm to settle on a different point in parameter space. It is best practice to keep early-stopping configurations identical across runs if consistent performance comparisons are required.

Are there scenarios where automatic hardware optimizations interfere with reproducibility?

Modern hardware, especially GPUs, offers mechanisms such as dynamic performance scaling, GPU overclocking, or CPU speed boost states. The hardware might re-schedule threads or operations to optimize throughput, but not necessarily in a deterministic order. This can reorder floating-point operations, leading to slight numerical differences across runs. Also, if one run triggers a thermal throttling event because the GPU overheated, another run might have a slightly different number of concurrency threads or scheduling behavior. While these effects are often minor, they can be magnified in large-scale training scenarios over thousands of batches.

On some systems, even the presence of other processes contending for GPU or CPU resources can alter the concurrency patterns. In multi-tenant environments (common in large organizations), different users or background tasks might occasionally launch on the same hardware, changing the scheduling timeline. Typically, these differences are small, but they can become relevant in tightly controlled experiments requiring absolute reproducibility. If so, the user might need to run on dedicated hardware in single-tenant mode, or restrict other processes entirely.

Could interpretability methods or model explainability tools reveal differences in model behavior despite similar metrics?

It is entirely possible for two models to achieve nearly identical accuracy or other global performance metrics but vary in the way they make decisions. Interpretation methods such as LIME or SHAP might show that each model attributes importance to different features or focuses on slightly different patches in an image classification task. In deep neural networks, feature visualization or gradient-based saliency maps may reveal these subtle differences in learned representations.

Even small variations in parameter initialization or data order can cause the model to rely on different sets of parameters to solve the same classification or regression problem. This can be quite relevant when domain experts or auditors want to understand how the model is making its decisions. Although metrics might appear consistent, the underlying learned patterns can differ. This underscores the importance of not only focusing on final success rates but also considering the consistency and transparency of the model’s decision-making processes.